Radio Frequency IDentification (RFID) used in business applications and international business management fields can create and sustain the competitive advantage, which is also one of the wireless telecommunication techniques for recognizing objects to realize Internet of Things (IoT) technologies. In construction of IoT network, the RFID technologies play the role of the front-end data collection via tag identification, as the basis of IoT. Hence, the adoption of RFID technologies is spurring innovation and the development of the IoT. However, in RFID system, one of the most important challenges is the collision resolution between the tags when these tags transmit their data to the reader simultaneously. Hence, in this paper I develop an efficient scheme to estimate the number of unidentified tags for Dynamic Framed Slotted Aloha (DFSA) based RFID system, with the view of increasing system performance. In addition to theoretical analysis, simulations are conducted to evaluate the performance of proposed scheme. The simulation results reveal the proposed scheme works very well in providing a substantial performance improvement in RFID system. The proposed algorithm promotes business effectiveness and efficiency while applying the RFID technologies to IoT.

Due to the rapid grow up of transaction volume of derivatives in the financial market, the Black-Scholes options pricing model (BSM) is played an important role recently and widely applied in various options contract. However, this theoretical model limited by the influences of many unexpected real world phenomena caused due to its six unreasonable assumptions, which often make the miss-pricing result because of the difference of market convention in practical. If we were to soundly take these phenomena into account, the pricing error could be reduced. In this paper, we provide a signal-decomposition oriented framework via wavelet analysis to improve the precision of BSM using integrated wavelet-based feature extraction with support vector machines (WSVMs). We investigate the techniques for transforming the noticeable signal from the mark to market price into estimating the option fair value and hence gain better precision estimation then pure support vector machine, in which has recently been introduced as a new technique for solving a variety of time series forecasting. Compare with the original GARCH method, adaptive neural-based fuzzy inference system (ANFIS) and pure SVMs, the performance of the presented method show the best. Using evidence from the warrants market in Taiwan, it supports our claims. This paper helps to provide an alternative way to refine the options valuation.

No abstract

No abstract