Relation between bit and symbol error probabilities for frequencyshift keying with limiter-discriminator detection

In M-ary, Gray coded frequency shift keying with limiter discriminator detection the bit error probability is usually approximated by the symbol error probability divided by the number of bits in a symbol. This approximation is known to be excellent for phase shift keying with large energy-to-noise ratios and Gaussian channel. In other cases this may be questionable. The authors compute the exact values of bit error probability as a function of energy-to-noise-ratio per bit for Gaussian, Rayleigh and Rician channels. They also compute the relative error involved in this approximation     

FSK With Limiter-Discriminator Detector in Nakagami Fading Channels With and Without Selection Combining

In this paper, an equation for the error probability of M-ary frequency shift keying with limiter-discriminator detection in Nakagami fading channels for arbitrary m is derived. The authors do the same for selection combining with L diversity channels for integer m and for switch and stay combining with two diversity channels for m=1 (Rayleigh fading). The error probability for various values of m, L, frequency deviation, and filter bandwidth is computed     

Performance of four-dimensional digital communication system infading channels

The authors derive a formula for the bit-error probability (BEP) of a four-dimensional signal and coherent but simple detector in fading channels with Rician or Nakagami probability density function for the received signal envelope. They compute the BEP in both cases and show its dependence on the energy-to-noise ratio per bit, E_b/N_o and channel parameters, K and m, respectively     

On the “dimensionality curse” and the“self-similarity blessing”

Spatial queries in high-dimensional spaces have been studied extensively. Among them, nearest neighbor queries are important in many settings, including spatial databases (Find the k closest cities) and multimedia databases (Find the k most similar images). Previous analyses have concluded that nearest-neighbor search is hopeless in high dimensions due to the notorious “curse of dimensionality”. We show that this may be overpessimistic. We show that what determines the search performance (at least for R-tree-like structures) is the intrinsic dimensionality of the data set and not the dimensionality of the address space (referred to as the embedding dimensionality). The typical (and often implicit) assumption in many previous studies is that the data is uniformly distributed, with independence between attributes. However, real data sets overwhelmingly disobey these assumptions; rather, they typically are skewed and exhibit intrinsic (“fractal”) dimensionalities that are much lower than their embedding dimension, e.g. due to subtle dependencies between attributes. We show how the Hausdorff and Correlation fractal dimensions of a data set can yield extremely accurate formulas that can predict the I/O performance to within one standard deviation on multiple real and synthetic data sets     

Quantifiable data mining using ratio rules

Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [AIS93b, SA96]. We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the “goodness” of a set of discovered rules. We also propose the “guessing error” as a measure of the “goodness”, that is, the root-mean-square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. Another contribution is a novel method to guess missing/hidden values from the Ratio Rules that our method derives. For example, if somebody bought $10 of milk and $3 of bread, our rules can “guess” the amount spent on butter. Thus, unlike association rules, Ratio Rules can perform a variety of important tasks such as forecasting, answering “what-if” scenarios, detecting outliers, and visualizing the data. Moreover, we show that we can compute Ratio Rules in a single pass over the data set with small memory requirements (a few small matrices), in contrast to association rule mining methods which require multiple passes and/or large memory. Experiments on several real data sets (e.g., basketball and baseball statistics, biological data) demonstrate that the proposed method: (a) leads to rules that make sense; (b) can find large itemsets in binary matrices, even in the presence of noise; and (c) consistently achieves a “guessing error” of up to 5 times less than using straightforward column averages. Received: March 15, 1999 / Accepted: November 1, 1999     

Efficient approximation of correlated sums on data streams

In many applications such as IP network management, data arrives in streams and queries over those streams need to be processed online using limited storage. Correlated-sum (CS) aggregates are a natural class of queries formed by composing basic aggregates on (x, y) pairs and are of the form SUM{g(y) : x /spl les/ f(AGG(x))}, where AGG(x) can be any basic aggregate and f(), g() are user-specified functions. CS-aggregates cannot be computed exactly in one pass through a data stream using limited storage; hence, we study the problem of computing approximate CS-aggregates. We guarantee a priori error bounds when AGG(x) can be computed in limited space (e.g., MIN, MAX, AVG), using two variants of Greenwald and Khanna's summary structure for the approximate computation of quantiles. Using real data sets, we experimentally demonstrate that an adaptation of the quantile summary structure uses much less space, and is significantly faster, than a more direct use of the quantile summary structure, for the same a posteriori error bounds. Finally, we prove that, when AGG(x) is a quantile (which cannot be computed over a data stream in limited space), the error of a CS-aggregate can be arbitrarily large.     

House Prices as a Result of Trading Activities: A Patient Trader Model

Computational Economics - We present a new modeling approach for house price movements as a consequence of the trading behavior of market agents. In our modeling approach, all agents are assumed to...     

The influence of a corrosive wood-cutting environment on the mechanical properties of hardmetal tools

The influence of a corrosive wood-cutting environment on the strength of hardmetals and on their behaviour under static loading conditions has been investigated. Two commercial hardmetals were tested, which differed in hard phase composition. The results show exposure to this environment to have a highly detrimental influence on the strength of both hardmetals investigated, due to localised corrosive attack which results in the formation of stress raisers. The relative loss in strength as a function of corrosion time is the same for both hardmetals. However, the performance of the two grades differ significantly when a static load is applied in this corrosive environment: the strength of the WC-Co grade remains unaffected by the applied load, while the grade containing a mixed carbide phase exhibits stress corrosion cracking which results in a further reduction in strength.