Previous studies have observed that TCP pacing evenly spacing out packets- minimizes traffic burstiness, reduces packet losses, and increases throughput. However, the main drawback of pacing is that the number of flow...
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ISBN:
(纸本)9781728118642
Previous studies have observed that TCP pacing evenly spacing out packets- minimizes traffic burstiness, reduces packet losses, and increases throughput. However, the main drawback of pacing is that the number of flows and the bottleneck link capacity must be known in advance. With this information, pacing is achieved by manually tuning sender nodes to send at rates that aggregate to the bottleneck capacity. This paper proposes a scheme based on programmable switches by which rates are dynamically adjusted. These switches store the network's state in the data plane and notify sender nodes to update their pacing rates when the network's state changes, e.g., a new flow joins or leaves the network. The scheme uses a custom protocol that is encapsulated inside the IP Options header field and thus is compatible with legacy switches (i.e., the scheme does not require all switches to be programmable). Furthermore, the processing overhead at programmable switches is minimal, as custom packets are only generated when a flow joins or leaves the network. Simulation results conducted in Mininet demonstrate that the proposed scheme is capable of dynamically notifying hosts to adapt the pacing rate with a minimum delay, increasing throughput, mitigating the TCP sawtooth behavior, and achieving better fairness among concurrent flows. The proposed scheme and preliminary results are particularly attractive to applications such as Science DMZ, where typically a small number of large flows must share the bandwidth capacity.
In this paper, we present a new approach for online joint detection and tracking for multiple targets, using sequential Monte Carlo methods. We first use an observation clustering algorithm to find some regions of int...
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In this paper, we present a new approach for online joint detection and tracking for multiple targets, using sequential Monte Carlo methods. We first use an observation clustering algorithm to find some regions of interest (ROIs), and then propose to initiate a new target or remove an existing track, based on the persistence information of these ROIs over time. In addition, we also integrate a very efficient 2-D data assignment algorithm into the sampling method for the data association problem. Computer simulations demonstrate that the proposed approach is robust in performing joint detection and tracking for multiple targets even though the environment is hostile in terms of a high clutter rate and a low target detection probability.
One of the popular and extensively used classification algorithms in the data mining and the machine learning technique is the support vector machine (SVM). Yet, conversely they have been traditionally applied to a sm...
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Low fold poorly sampled vintage seismic data often suffers from poor fault imaging. This can have a critical impact on reserve estimation and well planning. Acquiring high density seismic data over producing fields re...
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ISBN:
(纸本)9781613996720
Low fold poorly sampled vintage seismic data often suffers from poor fault imaging. This can have a critical impact on reserve estimation and well planning. Acquiring high density seismic data over producing fields requires overcoming logistic challenges along with additional costs and increased acquisition time. However, advances in seismic processing technology could improve the fault resolution of vintage seismic data in a cost effective manner. This has been proven in a case study from offshore Abu Dhabi. The presence of strong surface wave energy, resulting from the shallow water environment and near surface heterogeneity, masked events in the deeper part of the section. Poor and irregular spatial sampling caused aliasing of the surface wave. In the vintage processing, strong de-noising was applied to tackle the aliasing issue, which smeared the fault definitions. During the re-processing, a joint low-rank and sparse inversion was applied to regularize and densify the input data to obtain a de-aliased surface wave noise model. Subsequent adaptive subtraction of the noise from the input removed strong surface waves without damaging the body waves. The stack quality was improved by application of cascaded surface wave attenuation algorithms. Additional five dimensional Fourier reconstructions of the data improved the signal quality. A carefully designed fault-preserving residual noise attenuation workflow further reduced the residual noise content. Automatic picking of key stratigraphic horizons was carried out in order to evaluate the spatial resolution of the re-processing outcome. Sharper discontinuities along fault planes observed compared to the interpretation of the vintage seismic data. Increased confidence in fault interpretation is of value for structural restoration study and further reservoir understanding. In addition, several new, previously not-visible, small fault features were highlighted as evident from volumetric curvature and semblance analysis
The 3700km2 Nordkapp Basin area, Barents Sea, was recently acquired with a wide-spread source-over-spread design. With its 6 sources sitting on top of 18 multi-sensor streamers, one sail-line can record a dense carpet...
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In addressing the detection challenge of smalltargets with low echo intensity and high noise intensity in underwater environments with active sonar and random reverberation, we propose a target detection method that ...
In addressing the detection challenge of smalltargets with low echo intensity and high noise intensity in underwater environments with active sonar and random reverberation, we propose a target detection method that integrates wavelet packet decomposition with an improved convolutional neural network. This approach employs interframe accumulation processing to suppress and remove strong echoes and clutter interference, such as static reverberation and random burst noise, from the sonar background. Leveraging the characteristics of sonar echo decomposition through wavelet packet analysis, we process simulated underwater acoustic signals from four categories, including divers, underwater mines, metal plates, and oceanic noise interference, after interframe accumulation noise reduction. We then statistically analyze the energy distribution of echoes in various frequency bands based on wavelet packet decomposition. We extract the energy features from different frequency bands to enable the improved CNN to detect targets even at low SNRs. The results validate the feasibility and effectiveness of this algorithm.
In this paper we treat ultrasound image data as a two dimensional autoregressive (AR) signal. The image is modelled as consisting of distinct regions each described by one of a small number of AR models. Segmentation ...
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The low, slow, and small (LSS) target detection has always been a problem for both ground-based and air-borne radar systems. The factors such as weak target reflection energy, ground clutter, and random Doppler compon...
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The most fundamental problem in radar signal is detection of an object or physical phenomenon. This problem became more challenging to the signalprocessing and radar communities to detect smalltargets(like small ice...
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The most fundamental problem in radar signal is detection of an object or physical phenomenon. This problem became more challenging to the signalprocessing and radar communities to detect smalltargets(like small icebergs) in an oceanic environment. These smalltargets are difficult to detect for a marine radar system since they protrude only about a meter or so above the sea surface level. Unfortunately these smalltargets can cause severe damage to ships traveling in ice-ridden waters. In this paper, we make detection decision for smalltargets such as small fragments of icebergs using crosscorrelation instead of statistical decision theory. Then we recover a transmitted signal using matched filter. Our simulation results give the exact result even when the received signal is attenuated more than 90%.
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