Practical, low complexity time of arrival (TOA) estimation method with high accuracy are attractive in ultra wideband (UWB) ranging and localization. In this paper, a generalized maximum likelihood energy detection (GML-ED) ranging method is proposed and implemented. It offers low complexity and can be applied in various environments. An error model is first introduced for TOA accuracy evaluation, by which the optimal integration interval can be determined. Aiming to suppress the significant error created by the false alarm events, multiple pulses are utilized for accuracy promotion at the cost of extra energy consumption. For this reason, an energy efficiency model is also proposed based on the transmitted pulse number. The performance of the analytical research is evaluated and verified through practical experiments in a typical indoor environment.

In this paper, we investigate the throughput efficiency of the Go-Back-N ARQ protocol on parallel multiple channels with burst errors. We assume that packet errors occur according to a two-state Markov chain on each channel. The effect of the decay factor of the Markov chain on throughput efficiency is evaluated based on the results of numerical analysis.

In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.

Computers are increasingly used for implementing control algorithms in safety-critical embedded applications, such as engine control, braking control and flight surface control. Consequently, computer errors can have severe impact on the safety of such systems. Addressing the coupling of control performance with computer related errors, this paper develops a methodology for analyzing the impacts data errors have on control system dependability. The impact of a data error is measured as the resulting control error. We use maximum bounds on this measure as the criterion for control system failure (i.e., if the control error exceeds a certain threshold, the system has failed). In this paper we a) develop suitable models of computer faults for analysis of control level effects and related analysis methods, and b) apply traditional control theory analysis methods for understanding the impacts of data errors on system dependability. An automobile slip-control brake-system is used as an example showing the viability of our approach.

A schedule for a parallel program is said to be 1-fault-secure if a system that uses the schedule can either produce correct output for the program or detect the presence of any faults in a single processor. Although several fault-secure scheduling algorithms have been proposed, they can all only be applied to a class of tree-structured task graphs with a uniform computation cost. Besides, they assume a stringent error model, called the redeemable error model, that considers extremely unlikely cases. In this paper, we first propose two new plausible error models which restrict the manner of error propagation. Then we present three fault-secure scheduling algorithms, one for each of the three models. Unlike previous algorithms, the proposed algorithms can deal with any task graphs with arbitrary computation and communication costs. Through experiments, we evaluate these algorithms and study the impact of the error models on the lengths of fault-secure schedules.

This paper treats a fault detection/location of multi-processor systems, and we present a checking scheme based on Modified Processor-Data (MPD) graph with considering an error generation/propagation model for Algorithm-Based Fault Tolerant (ABFT) systems. The error propagation model considered here allows a computation result with multiple (more than one) erroneous inputs to be either erroneous or error-free. Also a basic algorithm for constructing checks for single-fault locatable/two-fault detectable ABFT systems based on the checking scheme is described with design examples.