Radio frequency identification (RFID) has been gaining popularity for inventory control, object tracking, and supply-chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. Prior works focus on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that for an RFID reader to access tags in a large area, active tags are likely to be used due to their longer operational ranges. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Hence, conserving energy for active tags becomes critical. Some prior works have studied how to reduce energy expenditure of an RFID reader when it reads tag IDs. We study how to reduce the amount of energy consumed by active tags during the process of estimating the number of tags in a system. We design two energy-efficient probabilistic estimation algorithms that iteratively refine a control parameter to optimize the information carried in transmissions from tags, such that both the number and the size of transmissions are reduced. These algorithms can also take time efficiency into consideration. By tuning a contention probability parameter$\omega$, the new algorithms can make tradeoff between energy cost and estimation time.

RFID has been gaining popularity for inventory control, object tracking, and supply chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. The prior work focuses on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that, for a RFID reader to access tags in a large area, active tags are likely to be used. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Unlike the prior work, this paper studies the RFID estimation problem from the energy angle. Our goal is to reduce the amount of energy that is consumed by the tags during the estimation procedure. We design several energy-efficient probabilistic algorithms that iteratively refine a control parameter to optimize the information carried in the transmissions from the tags, such that both the number and the size of the transmissions are minimized.

BACKGROUND: A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are often used to quantify the significance of these differences. The genes with small p-values are then picked as the genes responsible for the differences in the tissue RNA expressions. One key question is what should be the threshold to consider the p-values small. There is always a trade off between this threshold and the rate of false claims. Recent statistical literature shows that the false discovery rate (FDR) criterion is a powerful and reasonable criterion to pick those genes with differential expression. Moreover, the power of detection can be increased by knowing the number of non-differential expression genes. While this number is unknown in practice, there are methods to estimate it from data. The purpose of this paper is to present a new method of estimating this number and use it for the FDR procedure construction. RESULTS: A combination of test functions is used to estimate the number of differentially expressed genes. Simulation study shows that the proposed method has a higher power to detect these genes than other existing methods, while still keeping the FDR under control. The improvement can be substantial if the proportion of true differentially expressed genes is large. This procedure has also been tested with good results using a real dataset. CONCLUSION: For a given expected FDR, the method proposed in this paper has better power to pick genes that show differentiation in their expression than two other well known methods.

The purpose of the article is to implement the idea of using testability to estimate software reliability. The basic steps involve estimating testability, evaluating how well software was written, and assessing the effectiveness of testing. Results from these steps along with operational profiles are used to estimate software reliability. The paper describes an application of this method to evaluate the reliability of a real software system of about 6000 lines of executable code and discusses the results of such an estimation. The results are also compared with those obtained by using two reliability growth models.