In this paper, it is investigated the processes for automatic identification of the targets without personnel intervention in wireless multimedia sensor networks. Methods to extract the features of the object from the...
详细信息
In this paper, it is investigated the processes for automatic identification of the targets without personnel intervention in wireless multimedia sensor networks. Methods to extract the features of the object from the multimedia data and to classify the target type based on the extracted features are proposed within the scope of this study. The success of the proposed methods are tested by implementing a Matlab application and the results are presented in this paper.
Current algorithms for compressing genomic data mostly focus on achieving high levels of effectiveness and reasonable levels of efficiency, ignoring the need for features such as random access and stream processing. T...
详细信息
ISBN:
(纸本)9781479970896
Current algorithms for compressing genomic data mostly focus on achieving high levels of effectiveness and reasonable levels of efficiency, ignoring the need for features such as random access and stream processing. Therefore, in this paper, we introduce a novel framework for compressing genomic data, with the aim of allowing for a better trade-off between effectiveness, efficiency and functionality. To that end, we draw upon concepts taken from the area of media dataprocessing. In particular, we propose to compress genomic data as small blocks of data, using encoding tools that predict the nucleotides and that correct the prediction made by storing a residue. We also propose two techniques that facilitate random access. Our experimental results demonstrate that the compression effectiveness of the proposed approach is up to 1.91 bits per nucleotide, which is significantly better than binary encoding (3 bits per nucleotide) and Huffman coding (2.21 bits per nucleotide).
The reconstruction of gene regulatory networks (GRNs) helps to improve the understanding of underlying molecular mechanisms. Many important biological phenomena, such as genetic events involved in cancer proliferation...
详细信息
The reconstruction of gene regulatory networks (GRNs) helps to improve the understanding of underlying molecular mechanisms. Many important biological phenomena, such as genetic events involved in cancer proliferation, have been attributed to these correlated gene expressions. The identification of these interactions, some of which carry signatures to clinical relevant physiological effects, sheds light on the development of various clinical applications. For example, breast cancer metastasis can be inferred from the gene networks reconstructed from high throughput data. However, the DNA microarray data usually contain large number of genes but small number of samples, thus the incorporation of the extra dimension in time may lead to further complications in capturing the gene regulations due to the curse of dimensionality. This review focuses on introducing the signalprocessing community the concept of GRN reconstruction. In particular, we highlight state-of-the-art methodologies and the latest challenges in GRN reconstruction from short time course high throughput data.
Bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap in small-sample settings. However, its performance can deteriorate in the high-dimensional settings pr...
详细信息
ISBN:
(纸本)9781479970896
Bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap in small-sample settings. However, its performance can deteriorate in the high-dimensional settings prevalent in Genomic signalprocessing. We propose here a modification of Bolstered error estimation that is based on the principle of Naive Bayes. Rather than attempting to estimate a single variance parameter for a spherical bolstering kernel in high-dimensional spaces from a small sample, we assume an ellipsoidal kernel and estimate each univariate variance separately along each variable. In simulation results based on a model for gene-expression data and a linear SVM classification rule, the new bolstered estimator clearly outperformed the old one, as well as cross-validation and resubstitution, and was also superior to the 0.632 bootstrap except in the case where a large feature set is selected.
Separation between signal and noise, incoherent or coherent, is important in seismic dataprocessing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple r...
Separation between signal and noise, incoherent or coherent, is important in seismic dataprocessing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple reflections are a kind of coherent noise. In this research, we processed seismic data to attenuate multiple reflections in the both synthetic and real seismic data of Mentawai. There are several methods to attenuate multiple reflection, one of them is Radon filter method that discriminates between primary reflection and multiple reflection in the τ-p domain based on move out difference between primary reflection and multiple reflection. However, in case where the move out difference is too small, the Radon filter method is not enough to attenuate the multiple reflections. The Radon filter also produces the artifacts on the gathers data. Except the Radon filter method, we also use the Wave Equation Multiple Elimination (WEMR) method to attenuate the long period multiple reflection. The WEMR method can attenuate the long period multiple reflection based on wave equation inversion. Refer to the inversion of wave equation and the magnitude of the seismic wave amplitude that observed on the free surface, we get the water bottom reflectivity which is used to eliminate the multiple reflections. The WEMR method does not depend on the move out difference to attenuate the long period multiple reflection. Therefore, the WEMR method can be applied to the seismic data which has small move out difference as the Mentawai seismic data. The small move out difference on the Mentawai seismic data is caused by the restrictiveness of far offset, which is only 705 meter. We compared the real free multiple stacking data after processing with Radon filter and WEMR process. The conclusion is the WEMR method can more attenuate the long period multiple reflection than the Radon filter method on the real (Mentawai) seismic data.
IEEE 802.15.4a standard targets low data-rate wireless networks with extensive battery life and very low complexity. It has introduced impulse radio ultra-wideband (IR-UWB) as an emerging physical layer for energy-eff...
详细信息
IEEE 802.15.4a standard targets low data-rate wireless networks with extensive battery life and very low complexity. It has introduced impulse radio ultra-wideband (IR-UWB) as an emerging physical layer for energy-efficient communications. Low-power implementation of the digital baseband processing is critical for the design of an IR-UWB receiver. Thus high speed analog to digital converters (ADC) is needed. This paper presents link radio budget analysis of the receiver. Then ADC parameters needed for the design has been computed. We conclude that 3 bits Flash ADC presents the basic choice that provide sufficient resolution and high sampling rate required for the presented UWB receiver.
In a cognitive radio system, proper configuration of sensing duration enables maximization of the data throughput. However, no existing straightforward formula is available, which is desirable in the mobile context du...
详细信息
ISBN:
(纸本)9781479973408
In a cognitive radio system, proper configuration of sensing duration enables maximization of the data throughput. However, no existing straightforward formula is available, which is desirable in the mobile context due to tightness of computation time. A set of closed-form approximate formulae of optimal sensing duration for cognitive radio are derived under different signal to noise ratio (SNR). The approximation errors are perfectly small except within a certain SNR range, where the error is still less than 5%.
In this paper we introduce the concept of target visibility into the Interacting-Multiple-Model estimator with Probabilistic data Association Filter (IMMPDAF) and the Interacting-Multiple-Model estimator with Nearest ...
详细信息
In this paper we introduce the concept of target visibility into the Interacting-Multiple-Model estimator with Probabilistic data Association Filter (IMMPDAF) and the Interacting-Multiple-Model estimator with Nearest Neighborhood Filter (IMMNNF) in order to take into account those instances when the target becomes invisible and cannot be detected by the sensor. Tracks can be automatically terminated when the target becomes invisible or when it enters an area occluded by the physical limitations of the sensor. We employ the Natural Logarithm of the Dynamic Error Spectrum (NL-DES) to evaluate the performance of these filters. Results show that the visibility concept significantly improves the performance of the IMMNNF and IMMPDAF. This enhances the capability of platforms that employ the IMMNNF and/or IMMPDAF in tracking maneuvering targets. It also improves the ability of such platforms to detect and identify possible threats that may endanger a protected zone and consequently their ability to provide early warning of the presence of such threats.
This paper addresses the problem of RF-based wide-area human motion monitoring in indoor multipath environments. A major challenge for conventional pulse-Doppler radar in multipath scenarios is the difficulty in discr...
详细信息
ISBN:
(纸本)9781479928941
This paper addresses the problem of RF-based wide-area human motion monitoring in indoor multipath environments. A major challenge for conventional pulse-Doppler radar in multipath scenarios is the difficulty in discriminating direct-path targets from ghost returns due to multipath scattering. In this paper, the ability of a multiple-input multiple-output (MIMO) RF probe to discern both direction-of-departure (DoD) and direction-of-arrival (DoA) via "non-causal" beamforming is exploited for indoor motion monitoring. Preliminary results with real data are presented which demonstrate the sidelobe suppression and multipath mitigation achieved. Also, MIMO processing is analyzed using the bi-directional beampattern and spectrum defined herein.
Improvements in detecting weak targets from a small radar platform must come through increased temporal integration, i.e., extending the time over which target samples are coherently integrated. Conventional single-ch...
详细信息
Improvements in detecting weak targets from a small radar platform must come through increased temporal integration, i.e., extending the time over which target samples are coherently integrated. Conventional single-channel radar assumes a linear-phase signal model that is only accurate over a short dwell time for typical target motion. Over an extended dwell, the target signal includes multiple nonlinear phase components, each of whose effects become significant at different times during the dwell. An algorithm is presented that develops a multiphase signal model in multiple stages based on these times. A modification of the proposed approach improves the signal model for the most challenging targets. When used as the detection filter, the multiphase signal model yields near optimal performance over an extended dwell time for a wide range of target parameters. Typical improvement in output signal-to-noise ratio (SNR) for a 500 ms dwell is 12-13 dB over conventional processing.
暂无评论