New plane-sweep algorithms for distance-based join queries in spatial databases

Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource management, etc.). The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ). These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε). In this paper, we propose four new plane-sweep-based algorithms for KCPQs and their extensions for εDJQs in the context of spatial databases, without the use of an index for any of the two disk-resident data sets (since, building and using indexes is not always in favor of processing performance). They employ a combination of plane-sweep algorithms and space partitioning techniques to join the data sets. Finally, we present results of an extensive experimental study, that compares the efficiency and effectiveness of the proposed algorithms for KCPQs and εDJQs. This performance study, conducted on medium and big spatial data sets (real and synthetic) validates that the proposed plane-sweep-based algorithms are very promising in terms of both efficient and effective measures, when neither inputs are indexed. Moreover, the best of the new algorithms is experimentally compared to the best algorithm that is based on the R-tree (a widely accepted access method), for KCPQs and εDJQs, using the same data sets. This comparison shows that the new algorithms outperform R-tree based algorithms, in most cases.     

Neural-network-based adaptive baseband predistortion method for RF power amplifiers

An adaptive baseband predistortion method for RF power amplifier (PA) linearization is proposed and experimentally demonstrated. The predistortion component is implemented by a single-input dual-output multilayer perceptron (MLP). Both amplitude-to-amplitude and amplitude-to-phase distortion products are compensated by backpropagation training of the neural network including the response of the PA. Effects of modulator and demodulator imperfections on system performance are examined. Measurements on a system prototype reveal a significant linearity improvement that reaches 25 dB.     

A transmitter IC for TETRA systems based on a Cartesian feedback loop linearization technique

A fully integrated linearized transmitter for the next generation TETRA systems for PMR and public safety applications in a CMOS-based SiGe technology is described. The presented single-chip transmitter employs a Cartesian feedback loop technique in order to improve the linearity of the externally connected power amplifier. The transmitter is usable in a frequency range from 300 MHz up to 800 MHz, providing a linearity improvement of more than 40 dB.     

Buffer losses vs. deadline violations for ABR traffic in an ATM switch: A computational approach

The B‐ISDN will carry a variety of traffic types: the Variable Bit Rate traffic (VBR), of which compressed video is an example, Continuous Bit Rate traffic (CBR), of which telemetry is an example, Data traffic, and Available Bit Rate traffic (ABR) that represents aggregate data traffic with very limited guarantees on quality. Of these, VBR and CBR have timing constraints and need synchronous bandwidth; data traffic is relatively delay insensitive. In this paper, we consider the VBR, Data and ABR traffic types and obtain the cumulative distribution function (cdf) of the queueing delay experienced by a burst of ABR traffic in the output buffer of an ATM switch. The cdf is used to trade off buffer loss probabilities against deadline violation probabilities through adjusting the buffer size and (delay) deadline values. Large buffers result in low losses but queueing delays can become excessive and cause a high level of deadline violations. Both losses and violations are detrimental and an operating point must be chosen to achieve a balance. In this paper we study the nature of the trade off. We develop a stochastic Petri net model assuming periodic burst arrivals for VBR and Poisson arrival processes for the Data and ABR traffic types at the burst level, and solve the model analytically (numerically) using a decomposition approach. This decomposition, along with the inherent decomposability of the tagged customer approach for obtaining the cdf opens up a possibility of carrying out fast computations using a parallel machine for selecting the operating point each time that a call is admitted. This revised version was published online in June 2006 with corrections to the Cover Date.     

Digital libraries at a crossroads

Digital libraries constitute a relatively young scientific field whose life spans roughly the last 10 years. Despite its youth, the field has made a long journey from its initial conception to the present state of the art and has reached a level of maturity that warrants taking stock of its course so far and paving a path for the immediate and long-term future. In this paper, we analyse the recommendations of several activities that were initiated by the DELOS Network of Excellence under the Fifth Framework Programme of the European Commission and demonstrate that there are several issues that arise in similar form in most digital library efforts. We thus conclude that generic digital library management systems that address all these issues in a common fashion can and should be developed, so that future digital library efforts are able to build on such platforms and focus only on the hard issues that specialised functionality raises.     

Motorcycle riding under the influence of alcohol: Results from the SARTRE-4 survey

Riding a motorcycle under the influence of alcohol is a dangerous activity, especially considering the high vulnerability of motorcyclists. The present research investigates the factors that affect the declared frequency of drink-riding among motorcyclists in Europe and explores regional differences. Data were collected from the SARTRE-4 (Social Attitudes to Road Traffic Risk in Europe) survey, which was conducted in 19 countries. A total sample of 4483 motorcyclists was interviewed by using a face-to-face questionnaire. The data were analyzed by means of multilevel ordered logit models. The results revealed significant regional differences (between Northern, Eastern and Southern European countries) in drink-riding frequencies in Europe. In general, declared drinking and riding were positively associated with gender (males), increased exposure, underestimation of risk, friends’ behaviour, past accidents and alcohol ticket experience. On the other hand, it was negatively associated with underestimation of the amount of alcohol allowed before driving, and support for more severe penalties.     

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.     

High performance,low complexity cooperative caching for wireless sensor networks

During the last decade, Wireless Sensor Networks have emerged and matured at such point that they currently support several applications such as environment control, intelligent buildings, target tracking in battlefields. The vast majority of these applications require an optimization to the communication among the sensors so as to serve data in short latency and with minimal energy consumption. Cooperative data caching has been proposed as an effective and efficient technique to achieve these goals concurrently. The essence of these protocols is the selection of the sensor nodes which will take special roles in running the caching and request forwarding decisions. This article introduces two new metrics to aid in the selection of such nodes. Based on these metrics, we propose two new cooperative caching protocols, PCICC and scaPCICC, which are compared against the state-of-the-art competing protocol, namely NICoCa. The proposed solutions are evaluated extensively in an advanced simulation environment and the results confirm that the proposed caching mechanisms prevail over its competitor. The evaluation attests also that the best policy is always scaPCICC, achieving the shortest latency and the least number of transmitted messages.