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Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.

Information that propagates through social networks can carry a lot of false claims. For example, rumors on certain topics can propagate rapidly leading to a large number of nodes reporting the same (incorrect) observations. In this paper, we describe an approach for nding the rumor source and assessing the likelihood that a piece of information is in fact a rumor, in the absence of data provenance information. We model the social network as a directed graph, where vertices represent individuals and directed edges represent information ow (e.g., who follows whom on Twitter). A number of monitor nodes are injected into the network whose job is to report data they receive. Our algorithm identies rumors and their sources by observing which of the monitors received the given piece of information and which did not. We show that, with a sucient number of monitor nodes, it is possible to recognize most rumors and their sources with high accuracy.

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations.

Software bugs in distributed systems are notoriously hard to find due to the large number of components involved and the non-determinism introduced by race conditions between messages. This paper introduces Pop Mine, a tool for diagnosing corner-case bugs by finding the minimal causal directed acyclic graph (DAG) of events, spanning multiple processes, which captures a bug-triggering condition. Being based on causal order, a global notion of time is not required in uncovering bug-triggering distributed event patterns. Bug triggering event DAGs can be identified by comparing execution graphs from successful runs to those where bug manifestations were observed, and exposing the minimal discriminative event DAGs that may be responsible for the problem. This is a significant extension to prior debugging tools, in that prior work considered much simpler bug-triggering conditions such as single events, event sets, or ordered chains of events. To the authors' knowledge, this is the first paper that considers bug-triggering conditions in the form of distributed event graphs. To prove the effectiveness of our approach, we applied our tool to VCP, Chord and GreenGPS and diagnosed bugs. We also present performance analysis results to demonstrate the scalability of our approach.

New features are often added incrementally to avionics systems. This avoids redesign and recertification but still requires verifying the timing constraints of both new and existing applications. We introduce a new switch that facilitates this verification by bounding the latency of end-to-end communication across a network. Our clock-driven real-time switching algorithm is throughput-optimal with a bounded worst-case delay for all feasible traffic. Associated heuristics can verify whether the timing constraints of an avionics network are met, after new features have caused traffic to increase, and then search for alternative network configurations if necessary. We show how these heuristics cope with changes to an example environmental monitoring architecture within an avionics system that incorporates our switch. Our approach to analysis can be used to determine, quickly but rigorously, which system architecture meet timing constraints, and it allows the system architect to manage the cascading effects of component changes in a comprehensive manner.

The majority of existing wireless rate controls are based on the implicit assumption that frames are corrupted due to the random, arbitrary environmental and thermal noises. They generally reduce the channel rate on frame losses, trading lower efficiency in frequency band utilization for more robust modulation so that the current noise level may be tolerable. In highly interfered wireless networks where frames are lost mainly due to interference from other wireless transceivers, simply reducing the channel rate prolongs the frame transmission time and therefore aggravates frame loss ratio. This positive feedback in the rate control loop quickly diverges the interfered transceivers into a suboptimal channel rate and drives the network into a state with high interference. In the worst case, interfered transceivers can be starved. In this paper we present RAF, the rate-adaptive framing that jointly controls the channel rate and frame size according to the observed interference patterns and noise level at the receiver. Based on the inputs from physical layer carrier sense, the receiver derives the optimal channel rate and frame size that maximize throughput, and informs the transmitter of such optimal configuration in a few bits in the per-frame acknowledgement. Through intensive simulations we show that RAF consistently outperforms ARF, RBAR, and OAR in all simulated scenarios.