Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Minimal residual disease monitoring in multiple myeloma: a comparison between allelic-specific oligonucleotide real-time quantitative polymerase chain reaction and flow cytometry.

BACKGROUND AND OBJECTIVES: Minimal residual disease (MRD) studies are useful in multiple myeloma (MM). However, the definition of the best technique and clinical utility are still unresolved issues. The aim of this study was to analyze and compare the clinical utility of MRD studies in MM with two different techniques: allelic-specific oligonucleotide real-time quantitative PCR (ASO-RQ-PCR), and flow cytometry (FCM). DESIGN AND METHODS: Bone marrow samples from 32 MM patients who had achieved complete response after transplantation were evaluated by ASO-RQ-PCR, using TaqMan technology, and multiparametric FCM. RESULTS: ASO-RQ-PCR was only applicable in 75% of patients for a variety of technical reasons, while FCM was applicable in up to 90%. Therefore, simultaneous PCR/FCM analysis was possible in only 24 patients. The number of residual tumor cells identified by both techniques was very similar (mean=0.29%, range=0.001-1.61%, correlation coefficient=0.861). However, RQ-PCR was able to detect residual myelomatous cells in 17 patients while FCM only did so in 11; thus, 6 cases were FCM negative but PCR positive, all of them displaying a very low number of clonal cells (median=0.014%, range=0.001-0.11). Using an MRD threshold of 0.01% (10(-4)) two risk groups with significantly different progression-free survival could be identified by either PCR (34 vs. 15m, p=0.04) or FCM (27 vs. 10m, p=0.05). INTERPRETATION AND CONCLUSIONS: Although MRD evaluation by ASO-RQ-PCR is slightly more sensitive and specific than FCM, it is applicable in a lower proportion of MM patients and is more time-consuming, while both techniques provide similar prognostic information.

Integrated Optimal Power Flow for Distribution Networks in Local and Urban Scales

This paper develops an integrated optimal power flow (OPF) tool for distribution networks in two spatial scales. In the local scale, the distribution network, the natural gas network, and the heat system are coordinated as a microgrid. In the urban scale, the impact of natural gas network is considered as constraints for the distribution network operation. The proposed approach incorporates unbalance three-phase electrical systems, natural gas systems, and combined cooling, heating, and power systems. The interactions among the above three energy systems are described by energy hub model combined with components capacity constraints. In order to efficiently accommodate the nonlinear constraint optimization problem, particle swarm optimization algorithm is employed to set the control variables in the OPF problem. Numerical studies indicate that by using the OPF method, the distribution network can be economically operated. Also, the tie-line power can be effectively managed.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.