This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaus...
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This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be.
This paper addresses the passive detection of a common signal in two multi-sensor arrays. For this problem, we derive a detector based on likelihood theory for the case of one-antenna transmitters, independent Gaussia...
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In rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the develo...
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In rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the development of a sustainable water quality monitoring system is proposed. The green analytics principles were considered for developing the proposed system to reduce the measurement's time consumption and labor cost. Five water quality parameters [pH, oxidation reduction potential (ORP), dissolved oxygen (DO), electrical conductivity (EC), and temperature] have been measured using the developed system. The overall drinking water quality is measured by the proposed partial least squares regression (PLSR) model. The developed system's performance is determined by mean average percentage error (MAPE), root-mean-square error (RMSE), and R-2. The traceability of water quality sensors is defined with required uncertainty in water quality parameters. The measured uncertainty is 0.002, 0.892, 0.015, 0.029, and 0.017 for pH, EC, DO, ORP, and temperature, respectively. The relation between estimated and predicted water quality parameters (R-2 > 0.93) shows that the developed system can be a suitable replacement for traditional water quality monitoring techniques.
The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is ...
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The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.
Both dynamic and static visualizations of biphasic fluid have been the focus of attention that received wide notices. This work focused on carrying out research on solid-liquid biphasic visualization which was common ...
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Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in whic...
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Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.
multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensorarrays drastically increase efficiency in data collection. In particular, geom...
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multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensorarrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing methods developed for gridded data has been challenged by sensorarrays "roaming" in the landscape. In realization of the issue, the Innovative Geophysical Approaches for the Study of Early Agricultural Villages of Neolithic Thessaly (IGEAN) Project explored various innovative techniques for the betterment of the multi-sensor geomagnetic data processing. As a result, a modular pipeline is produced with minimal user intervention. In addition to standard steps, such as data clipping, various other algorithms have been introduced. This pipeline is tested over 20 Neolithic settlements in Thessaly, Greece, three of which are presented here in detail. The proposed workflow provides drastic improvements over raw data. As a result of these improvements, the IGEAN project revealed astonishing details on architectural elements, settlement enclosures, and paleolandscapes, changing completely the existing perspective of the Neolithic habitation in Thessaly.
An amperometric Bioelectronic Tongue is reported for glucose determination that contains eight sensor electrodes constructed using different metal electrodes (Pt, Au), oxidoreductase enzymes (glucose oxidase, ascorbat...
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作者:
Wu YueShi ChanghongYang WeiNorth Univ China
Natl Key Lab Elect Measurement Technol Taiyuan 030051 Peoples R China North Univ China
Mech & Elect Engn Coll Taiyuan 030051 Peoples R China PLA
Quartermaster Inst Gen Logist Dept Beijing 100010 Peoples R China
The multi-sensor array, such as the sound, light, infrared, vibration etc, is used to get the street lights environmental information. Combined with a variety of clock control strategy for control lamps, it can achiev...
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ISBN:
(纸本)9788993215021
The multi-sensor array, such as the sound, light, infrared, vibration etc, is used to get the street lights environmental information. Combined with a variety of clock control strategy for control lamps, it can achieve the background information of perception, detection, identification, and collect the typical characteristics of an effective signal, to rationally determine the threshold value range in the circuit design and lay a solid foundation for the realization of intelligent control of lights. The joint acquisition streetlights background signal through a variety of sensors carried out in a typical experimental design and preparation, principles, introduction, experimental methods and experimental analysis of the results, not only can test the design of control circuit, but also optimize the choice of the sensor models to provide true comparative data. In order to facilitate in a real environment to detect and further identify goals, to achieve the typical information data fusion of specific objectives, the ultimate street intelligent control can be achieved. Experimental results show that the magnetic sensors and infrared sensors detect close proximity, so the model of sensor needs to be replaced or further improved design (such as the optimization of pre-level filtering and amplification circuit design). Acoustic sensors and vibration sensors to detect distance, can meet night lighting control at residents of the community as well as tourist attractions. But there are a lot of distances to meet the use demands of roads and high-speed traffic, the more optimal designs and experiments is need. In addition, a range problem of information synchronize data collection, background noise and a variety of information data crosstalk need to be solved in the joint acquisition experiments, which in the paper were discussed.
Networked, multi-sensor array systems have proven to be advantageous in thesensor world. A large amount of research has been conducted with these systems, with amain interest in data fusion. Intelligently processing t...
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Networked, multi-sensor array systems have proven to be advantageous in the
sensor world. A large amount of research has been conducted with these systems, with a
main interest in data fusion. Intelligently processing the large amounts of data collected
by these systems is required in order to fully utilize the benefits of a multi-sensor array
system. A robust but flexible simulation environment would provide a platform for
accurately comparing current and future data fusion theories.
This thesis proposes a simulator model for testing fusion theories for these acoustic
multi-sensor networks. An iterative, lossless data fusion algorithm was presented as the
model for simulation development. The arrangement and orientation of objects in the
simulation environment, as well as most other system parameters are defined by the user
before the simulation runs. The sensor data, including noise, is generated at the
appropriate time delay and propagation loss before being processed by a delay and sum
beamformer and a matched filter. The resulting range-Doppler maps are modified to
probability density functions, and translated to a single point of reference. The data is
then combined into a single world model.
An iterative process is used to filter out false targets and amplify true target
detections. Data is fused from each multi-sensor array and from each simulation run.
Target amplitudes are gained if they are present in all combined world models, and are
otherwise reduced. This thesis presents the results of the fusion algorithm used, including
multiple iterations, to prove the algorithms effectiveness.
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