With the increase of wireless local area networks (WLAN) using the IEEE 802.11 standard, the interference between WLAN devices has become a significant problem. The IEEE802.11 standard includes a carrier-sense and collision avoidance mechanism, but it is not able to avoid some causes of collisions, including coincidental choice of identical random backoff count, or interfering transmission power below the carrier-sense threshold. It also does not distinguish the cause of frame loss at a receiver, and executes the same control countermeasures regardless of the cause of frame loss. We propose that identifying collision type by detecting and analyzing frame collision events is useful for WLAN managers and controllers to select appropriate countermeasures to reduce collisions. In particular, we consider the classification of collision type based on temporal features of received signal power. We present examples that show the relation between collision cause and collision type, and the feasibility of reducing collisions by changing transmissions parameters according to the collision type.
We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.
In this paper, we demonstrate a food recognition method by monitoring power leakage from a domestic microwave oven. Universal Software Radio Peripheral (USRP) is applied as a low-cost spectrum analyzer to measure the microwave oven leakage as received signal strength indication (RSSI). We aim to recognize 18 categories of food that are commonly cooked in a microwave oven. By analyzing 180 features that contain the information of heatingtime difference, we attain an average recognition accuracy of 82.3%. Using 138 features excluding the heating-time difference information, the average recognition accuracy is 56.2%. The recognition accuracy under different conditions is also investigated, for instance, utilizing different microwave ovens, different distances between the microwave oven and the USRP as well as different data down-sampling rates. Finally, a food recognition application is implemented to demonstrate our method.
