30132329
Evolutionary Programming in a Distributed Packet Scheduling Architecture
19457184
Efficient Queuing Scheme for Multicast Switches in Overlay Networks
6230033
High Performance ATM Network Simulators
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.
17500038
Frequency hopping pattern detection in wireless ad hoc networks
Frequency hopping is a technique that wireless devices communicate in a way that the transmission frequencies are alternated in a pre-determined ordered hopping pattern known only to the sending and receiving devices. In this paper, we develop an algorithm called frequency hopping pattern detection to detect the frequency hopping pattern of a wireless ad hoc network. The algorithm is performed in three stages. Stage 1 locates the first frequency in the pattern; stage 2 detects the remaining frequencies; and stage 3 calculates the entire hopping pattern. All three stages work together and guarantee the capture of all frequencies used, the pattern in which they occur, and the length of transmission for each frequency. The algorithm is simulated in a VHDL environment. Simulation results verify the correctness of the algorithm.
1481206
QoS Control and Performance Analysis of a Gigabit Network
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.
13467606
Mining Social Networks: Uncovering Interaction Patterns in Business Processes
Increasingly information systems log historic information in a systematic way. Workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called “event log”, i.e., a log recording the execution of activities. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach, defines metrics, and presents a tool to mine social networks from event logs.
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.
15123491
Business process mining: An industrial application
Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular.
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.