In order to estimate the parameters of multi Frequency-Hopping(FH) signals in the condition of non-cooperation and overcome the bottleneck of huge dataprocessing,a parameter estimation method based on compressive sap...
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In order to estimate the parameters of multi Frequency-Hopping(FH) signals in the condition of non-cooperation and overcome the bottleneck of huge dataprocessing,a parameter estimation method based on compressive saptial time-frequency joint analysis is *** the arbitrary compressive array structure is analyzed,then based on this structure we propose a method to estimate the direction of arrivals(DOAs) with only a small number compressive samplings by exploiting the spatial sparsity of multi FH *** by exploiting the sparsity in frequency domain,a spectrogram estimation algorithm is proposed by using the same compressive *** results show that this algorithm can effectively estimate multi Frequency-Hopping signals' DOAs and specrtograms with a tiny *** algorithm is lower in computation complexity,and can be very practical in real-time Frequency-Hopping signalprocessing.
Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-...
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ISBN:
(纸本)9780819497079
Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large- scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.
We present a data-driven formant model and methodology for discovering its parameters, namely phoneme targets and coarticulation functions for consonant-vowel-consonant (CYC) words from fully-automatic formant data. T...
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ISBN:
(纸本)9781479903566
We present a data-driven formant model and methodology for discovering its parameters, namely phoneme targets and coarticulation functions for consonant-vowel-consonant (CYC) words from fully-automatic formant data. The model uses formant targets that are speaker dependent, but independent of speaking style and phonemic context. We used a global error measure to search for optimal formant targets for all phonemes, including classes of sounds where formants are not directly observable. Analysis of coarticulation parameters found significant differences in parameters between clear and conversational speech. Estimated formant targets were largely in agreement with acoustic-phonetic expectations. An intelligibility test validated that resynthesized CYC words using modeled formant trajectories were nearly as intelligible as resynthesized CYC words using observed formant trajectories.
This paper describes a new directional borehole radar system and its field testing. The system uses a thin radar probe (57 mm diameter) and a circular dipole array directive antenna. The radar is of the step frequency...
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This paper describes a new directional borehole radar system and its field testing. The system uses a thin radar probe (57 mm diameter) and a circular dipole array directive antenna. The radar is of the step frequency type with a network analyzer. Through careful antenna design, we were able to achieve the compact radar probe and precise measurement at frequencies between 5 and 500 MHz. All the associated surface electronics for the radar system can be fit into a small carrying case. The radar probe includes a triaxial accelerometer, a triaxial compass, an angular velocity sensor and a thermometer. data from these sensors can be used to compensate for the rotation and inclination of the radar probe, and this enables us to locate reflection points in 3-D space correctly. All the data acquired by the radar probe were sent to the processing electronics via an optical link, and the data was updated in real time. Our field testing confirmed that system accuracy for determining arrival directions was better than 10 degrees between 30 and 180 MHz in wet soil. We demonstrated 3-D location of a buried cylindrical conducting object, which was set 2 m from the radar in wet soil. After system calibration and signalprocessing, we were able to estimate the reflection point position with an accuracy of 41 cm.
In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that t...
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ISBN:
(纸本)9780819497079
In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. This paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. The sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.
This paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the ...
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This paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the variety of the transmit powers between the macro and small cells, interference levels are different. The proposed solution hierarchically selects the strongest streams of each cell with a contribution to the sum rate, while constructing the streams via singular value decomposition (SVD). In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit channel and virtual receive channel of the selected stream. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell. It is shown that the proposed method can dynamically select more streams in heterogeneous networks and achieve higher data rates compared to the existing algorithms.
Scientific experiments and simulations produce mountains of data in file formats, such as HDF5, NetCDF, and FITS. Often, a relatively small amount of data holds the key to new scientific insight. Locating that critica...
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Scientific experiments and simulations produce mountains of data in file formats, such as HDF5, NetCDF, and FITS. Often, a relatively small amount of data holds the key to new scientific insight. Locating that critical information in these large files is challenging because existing solutions need significant user involvement in preparing the data, generating indexes, and answering queries. data management systems that support querying, such as SciDB, require a costly process of loading data from scientific data formats to these systems. The search results also need to be converted back to a format needed by the subsequent data analysis and visualization tools. These steps are time-consuming, tedious, and possibly error-prone. Toward providing efficient data management directly on these scientific file formats, we introduce a framework called Scientific data Services (SDS). SDS targets to provide efficient data management optimizations as services. In this paper, we introduce the design and implementation of one such service, the parallel querying service. To answer the queries efficiently, we transparently augment user data with bitmap indexes and ordered datasets. We design the querying service to manage these augmented datasets and to redirect queries automatically to bitmap indexes or to ordered datasets based on their availability and the expected query response time. The generation of bitmap indexes and sorted datasets and querying are parallelized to work on large supercomputers. We show that SDS achieves 22X, 55X, and 62X speedups compared to conventional full-scan approach of sifting through data in answering three queries from a plasma physics analysis application.
Todays wireless networks are mostly dominated by multimedia content, which poses much pressure on the macrocell networks. In such a case, underlaid small cells deployment is considered promising to off-load the traffi...
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Todays wireless networks are mostly dominated by multimedia content, which poses much pressure on the macrocell networks. In such a case, underlaid small cells deployment is considered promising to off-load the traffic from the overburdened macrocell network and meet the applications' quality of service (QoS). However, the interference management among dense deployment of small cells remains a technical challenge. To address this issue, a neighborhood cooperation based interference mitigation scheme is proposed in this paper. By forming coalitions, small cell access points (SAPs) are able to mitigate co-tier interference within a coalition and thus improve their achievable data rates. The cooperative behavior among the neighborhood SAPs is formulated as a coalition game in partition form with externalities. To achieve a final stable coalition structure in the recursive core, a distributed algorithm based on merge with partial reversibility rule is introduced and its performance is theoretically analyzed. Simulation results show that the proposed algorithm can substantially improve the individual SAP throughput and system payoff as well when compared to the classical scheme and non-cooperative case.
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computa...
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ISBN:
(纸本)9781479952106
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time consumption and memory usage for training SVMs. The proposed method is fully dynamic, which stores only a small fraction of previous training examples whereas the rest can be discarded. It can further handle unseen labels in new training batches. The classification experiments show that the proposed method achieves the same level of classification accuracy as batch learning while the computational cost is significantly reduced, and it can outperform other incremental SVM approaches for the new class problem.
Ultrasound is widely used in diagnostic applications where novel methods and techniques are continuously developed and proposed. The test of new techniques often requires the access to the raw echo-data saved from eac...
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Ultrasound is widely used in diagnostic applications where novel methods and techniques are continuously developed and proposed. The test of new techniques often requires the access to the raw echo-data saved from each of the multiple elements which compose the modern array probes. Given the high number of receiving elements and the high Analog-to-Digital sampling rate, tens of GB of data are typically generated in few seconds. Only a small number of research instruments are equipped to save raw data, but the saved quantity is often not sufficient. In this paper we describe a memory board that, coupled to the Ultrasound Advanced Open Platform (ULA-OP), can save up to 36 GB of data, sampled at 50 MHz, from 64 probe elements. Two novel applications developed by using the data from this board are discussed as well.
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