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Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in 10 cities. Results unveil the diversity of baseline communication activities across countries, but also provide evidence of the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses. [ABSTRACT FROM AUTHOR]

The Third International Conference on Data Engineering was held in Los Angeles, California on February 2 to 6, 1987. The conference was designed as an international forum for bringing together researchers, developers, managers, strategic planners and other users with an interest in the research, design and development of data engineering methodologies, strategies and systems. Its scope includes computer science, artificial intelligence, electrical engineering, and computer engineering. It featured papers from all major areas of data engineering, including database design and modeling, performance evaluation, algorithms, integrity, security, fault tolerance, query language, artificial intelligence approaches, knowledge bases, database machines, distributed databases and data engineering applications.

With the growing trend of extreme weather events in the Northeast U.S., a region of dense vegetation, evaluating hazard effects of wind storms on power distribution systems becomes increasingly important for disaster preparedness and fast responses in utilities. In this paper, probabilistic wind storm models for the study region have been built by mining 160-year storm events recorded in the National Oceanic and Atmospheric Administration's Atlantic basin hurricane database (HURDAT). Further, wind storms are classified into six categories according to NOAA criteria and IEEE standard to facilitate the evaluation of distribution system responses under different levels of hazards. The impacts of wind storms in all categories are accurately evaluated through a Sequential Monte Carlo method enhanced by a temporal wind storm sampling strategy. Extensive studies for the selected typical distribution system indicate that our models and methods effectively reveal the hazardous effects of wind storms in the study region, leading to useful insights towards building better system hardening schemes. [ABSTRACT FROM AUTHOR]

In this paper, a management strategy is developed to realize the real-time optimal torque distribution between the internal combustion (IC) engine and the electric motor of parallel hybrid electric vehicles (HEVs). Without depending on future information, a set of instantaneous cost functions is defined as the objective of a multiobjective problem, which includes vehicle energy consumption, selected emission species, and an evaluation criterion for the battery state of charge (SOC). The varying-domain method is then utilized to introduce a flexible priority among objectives and to transform the multiobjective problem into a nonlinear programming problem, the optimal solution of which is subsequently found by a genetic algorithm, i.e., GENOCOPIII. A comparison of the simulation results demonstrates the flexibility of the proposed varying-domain optimal management strategy (VOMS) under different driving conditions. Compared with the rule-based management strategy (RBMS) and the weighted sum management strategy (WSMS), the VOMS potentially improves the fuel economy, emission reduction, and stability of the SOC. [ABSTRACT FROM PUBLISHER]

This paper proposes the use of two indicators of the predictability of the load series along with an accuracy value such as mean average percentage error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models provide a description of the inner design of the model, the results from applying this model to a specific data base and the conclusions drawn from this application. However, a single accuracy value may not be sufficient to describe the performance of the model when applied to other data bases. The aim of this paper is to provide researchers with a tool that is able to assess the predictability of a load series and, therefore, contextualize the forecasting accuracy reported. Nine different data bases from the U.S. have been used; all of them include hourly load and temperature data. [ABSTRACT FROM PUBLISHER]

Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities (i.e., genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online. 1 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc. [ABSTRACT FROM AUTHOR]

The article presents an introduction to the special section related to International Conference on Software Engineering (ICSE'97), published in the August 1998 issue of the journal "IEEE Transactions on Software Engineering." Authors say that ICSE was held in Boston Massachusetts, in the week of May 17-23, 1997. The main goal of the conference was to meet needs and expectations of a large variety of attendees. The program committee of the conference was involved in a comprehensive reviewing process of papers submitted. At the end of this process, three papers were selected for publication. In the first paper, "Evaluating Testing Methods by Delivered Reliability," researchers discuss a method for evaluating the effectiveness of testing, based on a measure of the program reliability after testing. The second paper "Analyzing Partially Implemented Real-Time System," presents a technique for analyzing partially-implemented real-time systems. The third paper "Communication Metrics for Software Development," discusses results of comprehensive experiment whose goal was to evaluate the effectiveness of a set of communication metrics.

The Symposium on Foundations in Software Engineering is an annual conference sponsored by Association for Computing Machinery, a computer portal and dedicated to the presentation of innovative research results contributing to an engineering discipline for software systems. This special section of the July 1998 issue of the journal IEEE Transactions on Software Engineering features three of the best papers from the fourth symposium, held in October 1996 in San Francisco, California. During its first half-decade, research results presented at this conference have covered engineering research topics. However, a number of distinguishing properties tend to characterize the best of the papers and have helped set the standards for high-quality software engineering research. The three papers in this special section illustrate these properties. One of the critical challenges for software engineering research is to find practical tools that help system builder restructure existing systems. Mark Moriconi was general chair for the symposium and deserves considerable credit for its success.

This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions. Using this application, data were collected from youth athletes from 47 high schools and colleges in the Midwestern United States. In this paper, we focus on the design of a methodology to collect speech data, the extraction of various temporal and frequency metrics from that data, and the statistical analysis of these metrics to find patterns that are indicative of a concussion. Our results suggest a strong correlation between certain temporal and frequency features and the likelihood of a concussion. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Precipitation classification is a critical module in the retrieval algorithm set, for the dual-frequency precipitation radar (DPR) that will be onboard the global precipitation measurement (GPM) core satellite. Precipitation type classification namely stratiform, convective, and other rain type classification is an important part of the classification module. Characteristics of measured dual-frequency ratio ( DFRm), defined as the difference between measured reflectivity at two frequency channels (Ku- and Ka- band), were studied for different rain types. This paper shows that DFRm can be used to separate stratiform and convective rain. In this paper, a precipitation type classification model is developed for DPR profile classification using characteristics of DFRm. Data collected by the airborne PR (ARP-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. The performance of the precipitation classification method for the GPM-DPR resolution is evaluated and is shown to be applicable to the GPM resolution. [ABSTRACT FROM PUBLISHER]

The author reflects on the growth in government-funded research and development (R&D) activities in the U.S. and its impact on the publication of the "Proceedings of the IEEE." The R&D funding support evolved parallel to the number of papers submitted by engineers and scientists for publication in the journal after World War II. Research addressed the need to strengthen national security under the auspices of the National Science Foundation (NSF) and the U.S. Department of Defense (DOD).

The author discusses the status and authorship of the "IEEE Transactions on Antennas and Propagation" in the U.S. As an editor-in-chief, the author wish to improve the timing and quality of reviews from initial submission to final publication. The author also encouraged workers in the antennas and propagation field to publish their articles in the journal. In addition, the author raised some concerns about the importance of authorship and the basic aspects of it. Moreover, the author intends to raise awareness of responsibilities of the authors when they submit their manuscrips for publication.

Many of the sensors deployed at materials and test reactors cannot withstand the high flux/high temperature test conditions often requested by users at U.S. test reactors, such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory. To address this issue, an instrumentation development effort was initiated as part of the ATR National Scientific User Facility in 2007 to support the development and deployment of enhanced in-pile sensors. This paper provides an update on this effort. Specifically, this paper identifies the types of sensors currently available to support in-pile irradiations and those sensors currently available to ATR users. Accomplishments from new sensor technology deployment efforts are highlighted by describing new temperature and thermal conductivity sensors now available to ATR users. Efforts to deploy enhanced in-pile sensors for detecting elongation and real-time flux detectors are also reported, and recently-initiated research to evaluate the viability of advanced technologies to provide enhanced accuracy for measuring key parameters during irradiation testing are noted. [ABSTRACT FROM AUTHOR]

This brief paper describes the development of a pulse compression probing algorithm for real-time monitoring of nonlinear dynamic systems. The probing outputs of a system being monitored are estimates of the sequences of Markov parameters at the current operating point. By imposing signal to noise ratios at both the controlled outputs and the probing outputs, the probing inputs are designed to be non-intrusive and with the intention that the resulting probing outputs are unaffected by other applied signals to the system. The probing algorithm is implemented on a Virtex-5 field-programmable gate-array evaluation platform to expedite real-time processing, and is applied to monitoring the health of the NASA Generic Transport Aircraft Model (GTM). Samples of probing output residuals are evaluated using the Hotelling's T^2-test for change detection when significant noise is present in the system. This brief paper focuses its discussion on the method of rapid collection of independent samples to reduce detection delay. [ABSTRACT FROM PUBLISHER]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

This paper proposes a new framework in calculating the ex post prices for the spot electricity market. The proposed method finds a set of prices that are as consistent as possible with the actual outputs of resources correctly following their dispatch instructions. Two alternative consistency metrics are proposed in the paper. One measures the difference between the actual performance of an individual resource and its bid-based profit-maximizing response to the ex post prices in a perfectly competitive market. Another measures the difference between the ex post price and the price that supports the actual response of an individual resource. Compared to the existing approaches adopted in the real-time electricity markets in the U.S., the proposed method solves the ex post energy and ancillary service prices directly and simultaneously. It does not require heuristics that are hard to justify, and has a much clear definition for the pricing problem than the existing approaches do. Moreover, the proposed approach is flexible in the price control required by the market operators. Numerical examples are presented to further demonstrate the validity of this approach. [ABSTRACT FROM AUTHOR]

The article provides information on the manuscript by the Institute of Electrical and Electronics Engineers in the U.S. Manuscripts considered for publication can be contributed papers or letters to the editor. Key information on the Process for submission of a manuscript, style for submission of manuscript, review process, page charges and copyright are also presented.

The article discusses the highlights of the Antennas and Propagation Society (AP-S) Administrative Committee (AdCom) of the Institute of Electrical and Electronics Engineers (IEEE) Summer Meeting held in Charleston, South Carolina on May 31, 2009. The financial performance of the IEEE in 2008 was tackled. Other issues addressed were the need to improve membership outside the U.S., the IEEE budget for 2009 and 2010 and future conferences.

Ultra-wideband (UWB) signals exhibit different characteristics upon propagation through matter compared with narrow-band signals. The latter keeps a sinusoidal shape during different forms of signal propagation. The behavior of narrow- band signals does not apply to UWB signals in many cases. Presently, the possibilities for development of UWB signaling technology remain largely unexplored. Few applications have been developed due to strict regulations by the Federal Communications Commission (FCC). In this paper, we describe a series of experiments that have been carried out to determine the behavior of UWB signals and their properties. A transverse electromagnetic (TEM) horn antenna has been made for radiating UWB signals. A procedure for propagating UWB signals through a liquid medium of given salt concentration has been demonstrated, providing a basis for studying UWB signal propagation in biological matter. A new pulsewidth definition was adopted, which is suitable for propagated UWB signals. [ABSTRACT FROM AUTHOR]