Nikola Rožić

Energy Efficient Tag Estimation Method for ALOHA-based RFID systems

Radio frequency identification (RFID) technology has become an important tool for items identification and tracking. Its rapid deployment in dynamic industry areas enables variety of new applications in behavioral analysis and processes automation. In such systems it is crucial to identify all RFID tags before they leave the interrogation area. To accomplish fast identification, it is necessary to estimate a number of tags which are in the reader interrogation area. In this paper, we revisit the problem of tag quantity estimate and adapting frame size of dynamic frame slotted ALOHA widely used as RFID medium access control mechanism. The main disadvantage in the implementation of the state-of-the-art estimation algorithms includes the number of required computations, along with the temporary storage of large numbers which appear before estimation. As a consequence, such algorithms are energy inefficient and may require specific computer architecture to support the calculus. In order to address stated issues, we present improved linearized combinatorial model algorithm for optimal frame size selection which, due to linear property of the estimator, allows significant reduction in the estimate computation with acceptable tradeoff in accuracy. Simulations analyze the required number of slots to identify tag population and the floating point operation costs required to compute estimate. In addition, to emphasize the importance of reducing computational cost, we give a case study which compares energy consumed by the mobile RFID reader processor to compute the estimate and its energy equivalent of the required radio front-end energy to identify tags.

MIMO ARIMA models for handoff resource reservation in multimedia wireless networks

This paper presents a new approach to prediction of resource demand for future handoff calls in multimedia wireless IP networks. Our approach is based on application of multi-input-multi-output (MIMO) multiplicative autoregressive-integrated-moving average (ARIMA) (p,d,q)x(P,D,Q)S models fitted to the traffic data measured in the considered cell itself and on the new call admission control (CAC) algorithm that simultaneously maximizes the system throughput while keeping the handoff call dropping probability (CDP) below the targeted value. The main advantages of the proposed approach are the following: first, the proposed multi-variable prediction method gives on average better predictions (i.e. narrower prediction confidence interval) for realistic traffic situations, which results in lower new call blocking probability (CBP) at the targeted handoff CDP and second, the model is simple to implement since it does not require communication among the adjacent cells. Simulation results show the superiority of the proposed MIMO prediction approach combined with the proposed call admission control algorithm for some typical nonstationary situations in comparison with univariate models. Copyright

Biophysical Cochlear Model : Time-Frequency Analysis and Signal Reconstruction

The cochlea transforms incoming sound pressure into vibrations of the basilar membrane which give rise to neural impulses. Although it has been an object of research for several decades, complete understanding of its behavior is far from complete. Many experiments on real cochlea and many mathematical models have been developed in the past decades. In the biophysical cochlear model of Mammano and Nobili [1, 2], both mechanical and hydrodynamical aspects are treated at a level adequate to the complexity of realistic cochlear structures. In this paper, we analyze the time-frequency resolution of this biophysical cochlear model: analysis is first applied to the passive cochlear model and then extended to the active nonlinear model. Further, we propose the method for signal reconstruction from the cochlear time-frequency response. When applied to active cochlear model response, it could result with the signal which human ear actually hears (with some spectral enhancements and noise suppression).

An evaluation of the ABR explicit-rate allocation interfering with the guaranteed services traffic

Abstract In this paper, we evaluate operation of the available bit rate (ABR) explicit-rate allocation in a presence of variable background traffic of the guaranteed services. Specifically, impact of a switch averaging interval, long range-dependence of the guaranteed traffic, and network scaling are analyzed. Both single and multiple node network cases are considered with respective simulation models. Observed performance metrics of concern include mean queue length, utilization, and allocation fairness. On the basis of the obtained results, it is observed that for both single and multiple node networks a switch averaging interval close to the longest round-trip delay fairly meets the performance objectives of concern. In our previous work, we claim that long-range dependency (LRD) of the guaranteed services traffic could be transferred to the ABR traffic through the rate allocation. Hereby, it is shown that intensity of the transferred LRD decays as the network scaling increases.