Familiarity Discrimination of Radar Pulses

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k > 1 extension of NEN

Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network

The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications for dealing with such incomplete data are introduced, and performance is assessed on an emitter identification task using a data base of radar pulsesDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409) (S.G. and M.A.R); National Science Foundation (IRI-97-20333) (S.G.); Natural Sciences and Engineerging Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Comparison of Classifiers for Radar Emitter Type Identification

ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants- ARTMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP - is assessed with radar data gathered in the field. The k nearest neighbor (kNN) and radial basis function (RDF) classifiers are used for reference. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifers and comparable to that of kNN and RBF. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ART MAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT- feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.Defense Advanced Research Projects Agency and the Office of Naval Research (N000I4-95-1-409 (S.G. and M.A.R.); National Science Foundation (IRI-97-20333) (S.G.); Natural Science and Engineering Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Comparing Data Quality and Cost from Three Modes of On-Board Transit Passenger Surveys

This report presents the findings from a research project investigating the relative data quality and administration costs for three different modes of surveying bus passengers that produce results generalizable to the full passenger population. The three modes, all of which used survey methods distributed or administered onboard the transit vehicle, were: self-complete paper surveys, self-complete online surveys, and interviewer-assisted tablet-based surveys. Results from this study indicate several implications for practitioners choosing a survey mode. First, and most importantly, the analysis reinforces the point that there is no single, best survey mode. The choice of mode must depend on an agency’s priorities for what questions most need to be answered, what population groups are most important to represent, and exactly how the agency chooses to define concepts like a “complete” survey or a “usable” address. Findings suggest several general recommendations for current survey practice: (1) online surveys administered via an invitation distributed on the transit vehicle are not a good option; (2) old-fashioned, low-tech paper survey may still be the best option for many bus passenger surveys; (3) changes in survey results that accompany changes in survey methods should be interpreted with caution; and (4) using a new survey method, especially one relying on more complex technologies, may create unexpected glitches

Universal State Transfer on Graphs

A continuous-time quantum walk on a graph $G$ is given by the unitary matrix $U(t) = \exp(-itA)$, where $A$ is the Hermitian adjacency matrix of $G$. We say $G$ has pretty good state transfer between vertices $a$ and $b$ if for any $\epsilon > 0$, there is a time $t$, where the $(a,b)$-entry of $U(t)$ satisfies $|U(t)_{a,b}| \ge 1-\epsilon$. This notion was introduced by Godsil (2011). The state transfer is perfect if the above holds for $\epsilon = 0$. In this work, we study a natural extension of this notion called universal state transfer. Here, state transfer exists between every pair of vertices of the graph. We prove the following results about graphs with this stronger property: (1) Graphs with universal state transfer have distinct eigenvalues and flat eigenbasis (where each eigenvector has entries which are equal in magnitude). (2) The switching automorphism group of a graph with universal state transfer is abelian and its order divides the size of the graph. Moreover, if the state transfer is perfect, then the switching automorphism group is cyclic. (3) There is a family of prime-length cycles with complex weights which has universal pretty good state transfer. This provides a concrete example of an infinite family of graphs with the universal property. (4) There exists a class of graphs with real symmetric adjacency matrices which has universal pretty good state transfer. In contrast, Kay (2011) proved that no graph with real-valued adjacency matrix can have universal perfect state transfer. We also provide a spectral characterization of universal perfect state transfer graphs that are switching equivalent to circulants.Comment: 27 pages, 3 figure

Land tenure and environmental conditions at Wupperthal

Includes bibliography.This study documents a unique system of informal land tenure practised internally at the Moravian mission settlement of Wupperthal. The study analyses the system of tenure in terms of formal recognized systems, that exist or have existed in the past, and also in terms of the settlement's physical and social environment. Suggestions are made as to changes which could be introduced to ensure a constructive life-style for the inhabitants of Wupperthal, as well as the presentation of both the cultural and physical environment, so that future generations will be able to appreciate the beauty and tranquility of the village

Organic Evolution and the Capability Maturity of Business Intelligence

With the emergence of a new form of competition based on the extensive use of analytics, data, and subsequent decisionmaking, business intelligence (BI) has become a dominant platform for delivering solutions. The notion of gaining and sustaining competitive advantage through the use of complex analysis and data-intensive technologies has changed the way organizations manage themselves and compete in the marketplace. Initially, similar to other strategic technologies, BI will evolve or change depending on organizational needs and maturity. This suggests that process-oriented, descriptive, maturity models like the Capability Maturity Model apply. Despite the significance of BI, little attention has been given to examining the natural progression of business intelligence adoption and maturation within organizations. This is a concept paper presenting a model describing the relationship between evolution and the levels described by capability maturity. The proposed conceptual model is illustrated through the examination of a large, national, non-profit organization

A Workshop on Using NASA AIRS Data to Monitor Drought for the U.S. Drought Monitor

Recent studies indicate that drought indicators based on near-surface air relative humidity (RH), air temperature (T), and air vapor pressure deficit (VPD), derived from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA’s Aqua satellite can detect the onset of drought earlier than other drought indicators, specifically standardized precipitation index (SPI), which is widely used for drought onset detection. A recent study showed that standardized relative humidity index (SRHI) can detect drought signals earlier than SPI (Farahmand et al. 2015). Relative humidity is a climate variable defined as the ratio of air vapor pressure to saturated vapor pressure. Precipitation and relative humidity are related to each other in the sense that significant precipitation is not expected at low relative humidity. SRHI detected drought onset earlier or at the same time as SPI with a global average of approximately 0.6 (i.e., 60% of all events) and the mean lead time of 1.9 months. Also, SRHI successfully detected the early signs of the 2012 Midwestern drought, the 2011 Texas drought, and the 2010 Russian drought (Farahmand et al. 2015). In another study, standardized vapor pressure deficit (SVPD) and standardized temperature (ST) indicators from the AIRS mission have been shown to detect drought earlier or at the same time as SPI with an average lead time of 1.5 months and in 60% of events in the CONUS (Behrangi et al. 2016). VPD is an important climate variable, incorporating elements of both temperature and relative humidity. VPD is also a major controlling factor of evapotranspiration demand. With increasing air aridity, VPD increases which in turn indicates greater evaporation stress. Studies show that VPD reported increases during the formation and rapid intensification of drought conditions during the 2011 and 2012 drought events, suggesting that remotely sensed VPD holds considerable potential for drought early warning and assessment (Behrangi et al. 2015; Farahmand et al. 2021)