Statistical method of modeling and optimization for wireless sensor nodes with different interconnect technologies and substrates

A comparison study was carried out between a wireless sensor node with a bare die flip-chip mounted and its reference board with a BGA packaged transceiver chip. The main focus is the return loss (S parameter S11) at the antenna connector, which was highly depended on the impedance mismatch. Modeling including the different interconnect technologies, substrate properties and passive components, was performed to simulate the system in Ansoft Designer software. Statistical methods, such as the use of standard derivation and regression, were applied to the RF performance analysis, to see the impacts of the different parameters on the return loss. Extreme value search, following on the previous analysis, can provide the parameters' values for the minimum return loss. Measurements fit the analysis and simulation well and showed a great improvement of the return loss from -5dB to -25dB for the target wireless sensor node.

Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.

Reversal of impairment losses, firm performance and reporting incentives: Evidence from Malaysia

Malaysian Financial Reporting Standard (FRS) No. 136, Impairment of Assets, was issued in 2005. The standard requires public listed companies to report their non-current assets at no more than their recoverable amount. When the value of impaired assets is recovered, or partly recovered, FRS 136 requires the impairment charges to be reversed to its new recoverable amount. This study tests whether the reversal of impairment losses by Malaysian firms is more closely associated with economic reasons or reporting incentives. The sample of this study consists of 182 public companies listed on Bursa Malaysia (formerly known as the Kuala Lumpur Stock Exchange) that reported reversals of their impairment charges during the period 2006-2009. These firms are matched with firms which do not reverse impairment on the basis of industrial classification and size. In the year of reversal, this study finds that the reversal firms are more profitable (before reversals) than their matched firms. On average, the Malaysian stock market values the reversals of impairment losses positively. These results suggest that the reversals generally reflect increases in the value of the previously impaired assets. After partitioning firms that are likely to manage earnings and those that are not, this study finds that there are some Malaysian firms which reverse the impairment charges to manage earnings. Their reversals are not value-relevant, and are negatively associated with future firm performance. On the other hand, the reversals of firms which are deemed not to be earnings managers are positively associated with both future firm performance and current stock price performance, and this is the dominant motivation for the reversal of impairment charges in Malaysia. In further analysis, this study provides evidence that the opportunistic reversals are also associated with other earnings management manifestations, namely abnormal working capital accruals and the motivation to avoid earnings declines. In general, the findings suggest that the fair value measurement in impairment standard provides useful information to the users of financial statements.