1481206
QoS Control and Performance Analysis of a Gigabit Network
19457184
Efficient Queuing Scheme for Multicast Switches in Overlay Networks
15229138
Frequency Hopping Pattern Recognition Algorithms for Wireless Sensor Networks
Frequency Hopping Spread Spectrum (FHSS) as a communications technique has also been considered as one of security mechanisms to provide a good level of security against eavesdroppers in wireless sensor networking environment. We claim that the version presently used in wireless sensor communications does not yield a high level of security. In this paper, we develop algorithms to break the FHSS hopping patterns. Simulations are performed to verify the algorithms. Results suggest that wireless sensor networking is not secure in its current form.
6715244
Discovering Social Networks from Event Logs
Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.
16854565
ProM 4.0: Comprehensive Support for Real Process Analysis
This tool paper describes the functionality of ProM. Version 4.0 of ProM has been released at the end of 2006 and this version reflects recent achievements in process mining. Process mining techniques attempt to extract non-trivial and useful information from so-called "event logs". One element of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. Control-flow discovery is an interesting and practically relevant challenge for Petri-net researchers and ProM provides an excellent platform for this. For example, the theory of regions, genetic algorithms, free-choice-net properties, etc. can be exploited to derive Petri nets based on example behavior. However, as we will show in this paper, the functionality of ProM 4.0 is not limited to control-flow discovery. ProM 4.0 also allows for the discovery of other perspectives (e.g., data and resources) and supports related techniques such as conformance checking, model extension, model transformation, verification, etc. This makes ProM a versatile tool for process analysis which is not restricted to model analysis but also includes log-based analysis.
18823977
Ultrasound imaging simulation and echocardiographic image synthesis
Presents a ray tracing ultrasound imaging simulation method that accounts for the effects of reflection, scattering and attenuation. Two-dimensional echocardiographic images were synthesized using this method. Nonlinear effects introduced by the signal processing units in an ultrasound imaging system were also emulated. Three-dimensional triangular facet mesh models were employed in representing the heart structures and generating the synthetic two-dimensional echocardiographic images. The synthetic images agreed with the corresponding real ultrasound images in major ultrasound effects. No other prior work in echocardiographic image synthesis is known.
2139364
A spike sorting framework using nonparametric detection and incremental clustering
We introduce a statistical computing framework to address two important issues in spike sorting: flexible spike shape modeling and realtime spike clustering. In this framework, spikes are detected based on a nonparametric shape distribution; detected spikes are further grouped by an incremental clustering algorithm involving the second-order statistics-covariance matrix. We performed experiments on both simulated and real signals to study spike detection accuracy and cluster separation.