Filter Matrix Research Articles

Flow Injection-Traveling-Wave Ion Mobility-Mass Spectrometry for Prostate-Cancer Metabolomics.

Flow injection-traveling-wave ion mobility-mass spectrometry (FI-TWIM-MS) was applied to the nontargeted metabolic profiling of serum extracts from 61 prostate-cancer (PCa) patients and 42 controls with an analysis speed of 6 min per sample, including a 3 min wash run. Comprehensive data mining of the mobility-mass domain was used to discriminate species with various charge states and filter matrix salt-cluster ions. Specific criteria were developed to ensure correct grouping of adducts, in-source fragments, and impurities in the data set. Endogenous metabolites were identified with high confidence using FI-TWIM-MS/MS and collision-cross-section (CCS) matching with chemical standards or CCS databases. PCa patient samples were distinguished from control samples with good accuracies (88.3-89.3%), sensitivities (88.5-90.2%), and specificity (88.1%) using supervised multivariate classification methods. Although largely underutilized in metabolomics studies, FI-TWIM-MS proved advantageous in terms of analysis speed, separation of ions in complex mixtures, improved signal-to-noise ratio, and reduction of spectral congestion. Results from this study showcase the potential of FI-TWIM-MS as a high-throughput metabolic-profiling tool for large-scale metabolomics studies.

Sensor fault detection and isolation: a game theoretic approach

ABSTRACTThis paper studies sensor fault detection using a game theoretic approach. Sensor fault detection is considered as change point analysis in the coefficients of a regression model. A new method for detecting faults, referred to as two-way fault detection, is introduced which defines a game between two players, i.e. the fault detectors. In this new strategic environment, assuming that the independent states of the regression model are known, the test statistics are derived and their finite sample distributions under the null hypothesis of no change are derived. These test statistics are useful for testing the fault existence, as well as, the pure and mixed Nash equilibriums are derived for at-most-one-change and epidemic change models. A differential game is also proposed and solved using the Pontryagin maximum principle. This solution is useful for studying the fault detection problem in unknown state cases. Kalman filter and linear matrix inequality methods are used in finding the Nash equilibrium for the case of unknown states. Illustrative examples are presented to show the existence of the Nash equilibriums. Also, the proposed fault detection scheme is numerically evaluated via its application on a practical system and its performance is compared with the cumulative sum method.

Optimized weighted feature voting technique for content-based image retrieval

A content-based image retrieval system is proposed using an optimized weighted feature voting technique with multifeatures and multidistance measures. The proposed system consists of two phases: enrollment phase and querying phase. In enrollment phase, six features have been extracted from each enrollment image. These features are color histogram, edge histogram, edge direction histogram, hierarchical annular histogram, Gabor filter, and co-occurrence matrix. The extracted features are weighted, and feature weights are optimized using the four optimization techniques. Particle swarm optimization, ant lion optimizer, bird swarm algorithm, and whale optimization algorithm are applied and compared to decide the suitable technique. In querying phase, the same features are extracted from the query image and comparing these features to the extracted features from the database images through the matching measures. Three distance measures have been used and compared for the matching process: histogram intersection, Euclidean distance, and cosine distance. Based on these distances and the feature weights, the class of the query image is identified using the weighted feature voting mechanism. Finally, the related images are retrieved based on the feature that returned the maximum number of images in the identified class. For the validation and performance evaluations of the proposed system; Wang, Caltech101, and UW datasets are used. The experimental results show that the proposed method achieves improved precision in comparison with existing methods with 93% for Wang database, 92% for UW database, and 94% for Caltech101 database.

Source localization based on matrix filter and sparse asymptotic minimum variance

Wideband direction of arrival (DOA) estimation plays an important role in passive sonar signal processing. Recently, sparsity-based DOA estimation method has attracted considerable attention because of its high resolution in the condition of few snapshots and low signal-to-noise ratio. However, the localization accuracy is seriously affected by the interferences. Matrix filter (MF) has been widely used in passive sonar systems as a useful tool to passband the targets-of-interest while attenuating the interferences, but the output of the MF seriously affected subsequent DOA estimation when the power of the interferences after filtering is still stronger than the weak targets. In this paper, a method based on MF and sparse asymptotic minimum variance (SAMV) is given to localize the weak targets in a strong interference environment. The given method improves the ability of SAMV on weak targets localization and achieves high localization accuracy even in the condition that the power of the interferences after filtering remains stronger than the weak targets, which is verified by simulation and experimental results.

Actuator Fault Decoupled Residual Generation on Lateral Moving Aircraft

Implementation of time-scheduled maintenance is not suitable if it is applied for systems with many varieties of heavy workload and harsh environment since on that condition components degrade earlier that those under normal condition. Therefore it has been shifted to condition-based maintenance (CBM). One important aspect, among others, toward implementation of CBM method is fault isolation. The problem investigated in this paper is related todecouple residual generation for actuator fault isolation of an aircraft on lateral movement. The proposed solution for that problem is to implement combination of transformation matrix and special filter. Transformation matrix is used to convert feature locations of actuator faults to signature vectors. Moreover, the signature vectors will be processed further by special filter to generate decoupled residuals. It is assumed that the actuator is the only fault when the aircraft is on lateral movement. The result showed that special filter and transformation matrix can be designed so that the residual of aileron actuator fault is decoupled from the residual of rudder actuator fault.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Wideband sparse spatial spectrum estimation using matrix filter with nulling in a strong interference environment.

Wideband direction of arrival (DOA) estimation using a sensor array plays a fundamental role in passive sonar signal processing. Although sparsity-based DOA estimation methods can attain high resolution in the condition of few snapshots and low signal-to-noise ratio, the localization accuracy is seriously affected by strong interferences. In this paper, a matrix filter with nulling (MFN) is used to pass weak targets in sector-of-interest (passband) while attenuating the out-of-sector (stopband) interferences by forming deep nulls toward the directions of interferences adaptively. Then, a method based on sparse spectrum fitting (SpSF) and MFN is proposed to localize closely spaced wideband signals in a strong interference environment. In comparison with the minimum variance distortionless response and SpSF, the proposed method achieves higher localization accuracy, which is verified by simulation and experimental results.

Approximate iteration detection and precoding in massive MIMO

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%–83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provide enough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

Decoupled Convolutions for CNNs

In this paper, we are interested in designing small CNNs by decoupling the convolution along the spatial and channel domains. Most existing decoupling techniques focus on approximating the filter matrix through decomposition. In contrast, we provide a two-step interpretation of the standard convolution from the filter at a single location to all locations, which is exactly equivalent to the standard convolution. Motivated by the observations in our decoupling view, we propose an effective approach to relax the sparsity of the filter in spatial aggregation by learning a spatial configuration, and reduce the redundancy by reducing the number of intermediate channels. Our approach achieves comparable classification performance with the standard uncoupled convolution, but with a smaller model size over CIFAR-100, CIFAR-10 and ImageNet.

Tracking objects with co‐occurrence matrix and particle filter in infrared video sequences

Tracking objects in infrared video sequences became a very important challenge for many current tracking algorithms due to several complex situations such as illumination variation, night vision, and occlusion. This study proposes a new tracker that uses a set of invariant parameters calculated via the co-occurrence moments to better describe the target object. The usage of the co-occurrence moments gives the ability to exploit the information about the texture of the target to enhance the robustness of the tracking task. This latter is performed without any learning or clustering phase. The qualitative and quantitative studies on challenging sequences demonstrate that the results obtained by the proposed algorithm are very competitive in comparison to several state-of-the-art methods.

Design of Cancelable Palmprint Templates Based on Look Up Table

A novel cancelable palmprint templates generation scheme is proposed in this paper. Firstly, the Gabor filter and chaotic matrix are used to extract palmprint features. It is then arranged into a row vector and divided into equal size blocks. These blocks are converted to corresponding decimals and mapped to look up tables, forming final cancelable palmprint features based on the selected check bits. Finally, collaborative representation based classification with regularized least square is used for classification. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels. Meanwhile, it can also satisfy the needs of real-time applications.

Preparation of affordable and multifunctional clay-based ceramic filter matrix for treatment of drinking water

ABSTRACTAffordable clay-based ceramic filters with multifunctional properties were prepared using low-cost and active ingredients. The characterization results clearly revealed well crystallinity, structural elucidation, extensive porosity, higher surface area, higher stability, and durability which apparently enhance the treatment efficiency. The filtration rates of ceramic filter were evaluated under gravity and the results obtained were compared with a typical gravity slow sand filter (GSSF). All ceramic filters showed significant filtration rates of about 50–180 m/h, which is comparatively higher than the typical GSSF. Further, purification efficiency of clay-based ceramic filters was evaluated by considering important drinking water parameters and contaminants. A significant removal potential was achieved by the clay-based ceramic filter with 25% and 30% activated carbon along with active agents. Desired drinking water quality parameters were achieved by potential removal of nitrite (98.5%), nitrate (80.5%), total dissolved solids (62%), total hardness (55%), total organic pollutants (89%), and pathogenic microorganisms (100%) using ceramic filters within a short duration. The remarkable purification and disinfection efficiencies were attributed to the extensive porosity (0.202 cm3 g−1), surface area (124.61 m2 g−1), stability, and presence of active nanoparticles such as Cu, TiO2, and Ag within the porous matrix of the ceramic filter.

Nanocomposite filter made from porous mineral tuff with absorbed silver nanoparticles and its application for disinfection of water

Nanocomposite filters were developed by sorption of silver (Ag) nanoparticles into a matrix of household filters consisting of porous mineral tuff and their subsequent chemical deposition. The antimicrobial effects of the nanocomposite filter with Ag nanoparticles on Escherichia coli and Enterococcus faecalis and on a mixture of soil microorganisms were shown in water. The level of influence of Ag directly depended on its quantity to cause an inhibitory effect on bacteria; for each type of bacteria the amount of Ag causing antibacterial effect varied. Moreover, the efficiency of the filter for large-volume disinfection of water (up to 1,000 L) was determined. It depended on Ag nanoparticles’ quantity and the amount of microbial load. Nanocomposite filters from porous mineral tuff with adsorbed Ag nanoparticles have antibacterial properties and can be recommended for disinfection of a large volume of drinking water.

A Threshold-Parameter-Dependent Approach to Designing Distributed Event-Triggered H∞ Consensus Filters Over Sensor Networks.

This paper is concerned with distributed event-triggered H∞ consensus filtering for a discrete-time linear system over a sensor network. Different from some existing event-triggered communication schemes (ETCSs), a new distributed ETCS is first developed to reduce the communication frequency of neighboring sensors, where the threshold parameter in an event triggering condition is time-varying with attainable upper and lower bounds. Then a threshold-parameter-dependent approach is proposed to derive criteria for designing the desired H∞ consensus filters and the ETCS such that the resultant filtering error system is asymptotically stable with the prescribed H∞ performance while maintaining satisfactory resource efficiency. Furthermore, a polytope-like transformation with regard to time-varying threshold parameters is performed and a recursive algorithm is presented to determine the threshold-parameter-dependent filter matrix sequences and event triggering weighting matrix sequence. Two illustrative examples are employed to show the effectiveness of the developed approach.

Multiple frequencies multiple carriers

The OFDM scheme has the problems of out-of-band (OOB) leakage and low transmission efficiency in the time domain. This is because the scheme is a single frequency single carrier one in which one carrier is generated by one frequency. To overcome the problems we propose multiple frequencies multiple carriers (MFMC) scheme in which one carrier is generated using many frequencies. We establish a practical model which not only eliminates these disadvantages but also improves the transmission efficiency and peak-to-average power ratio (PAPR) performance. In the proposed MFMC scheme, the filter matrix is used in order to confine the OOB leakage of the spectrums of transmit signal, which is generated through processes such as jump-removing, filtering, and orthogonalizing from an initial matrix. MFMS is evaluated compared with OFDM in view of transmission efficiency, OOB leakage, and bit error rate performance.

Robust Adaptive Beamforming Based on Desired Signal Power Reduction and Output Power of Spatial Matched Filter

The performance of the conventional beamformers degrades in the presence of desired signal in the data samples and array steering vector (ASV) mismatch. Many beamformers have been proposed to improve the performance of standard Capon beamformer. However, the performance of these beamformers is affected by a number of factors, such as a number of data samples or sensors and signal-to-noise ratio. Moreover, the existing beamformers are also sensitive to the ASV mismatch of desired signal. In this paper, two robust adaptive beamformers are proposed to overcome the problems associated with these beamformers. The proposed beamformers have two pre-processing steps. First, the ASV of desired signal is estimated by computing the correlation between the nominal ASV and the eigenvectors corresponding to the dominant eigenvalues. Second, the power of desired signal in the sample covariance matrix is reduced by estimating the desired signal covariance matrix from the output power of spatial matched filter and noise covariance matrix. Subsequently, the matrix regularization is used to estimate the desired sample covariance matrix. In the first beamformer, the desired sample covariance matrix is constructed from the sample covariance matrix with the reduced desired signal power and the diagonal loading based on the output power of spatial matched filter, whereas in the second beamformer, the desired sample covariance matrix is constructed from the sample covariance matrix with the reduced desired signal power and the reconstructed interference-plus-noise matrix loaded with the output power of spatial matched filter. The proposed beamformers can provide a good performance in the presence of desired signal in the data samples and ASV mismatch as shown in the simulation results.

Photoacoustic imaging in scattering media by combining a correlation matrix filter with a time reversal operator

Acoustic scattering medium is a fundamental challenge for photoacoustic imaging. In this study, we reveal the different coherent properties of the scattering photoacoustic waves and the direct photoacoustic waves in a matrix form. Direct waves show a particular coherence on the antidiagonals of the matrix, whereas scattering waves do not. Based on this property, a correlation matrix filter combining with a time reversal operator is proposed to preserve the direct waves and recover the image behind a scattering layer. Both numerical simulations and photoacoustic imaging experiments demonstrate that the proposed approach effectively increases the image contrast and decreases the background speckles in a scattering medium. This study might improve the quality of photoacoustic imaging in an acoustic scattering environment and extend its applications.

Rapid sequential separation method for 210Po and actinides in air filter samples

This paper reports an analytical method for rapid sequential separation of 210Po and actinides (Pu, Am and U) in air filter samples. An extraction chromatographic column TEVA + TRU was used in the separation of 210Po and actinides from the filter matrix, followed by its determination using alpha spectrometry. TEVA resin is widely used in the separation of plutonium and neptunium from the environmental samples, in this work potential application of TEVA in the sequential separation of 210Po and plutonium from filter matrix has been evaluated. This new method is simple, robust and can be performed formed in a day.

2D DOA and Frequency Estimation Method with One Vector-Sensor and Two Pressure Sensors Based on ESPRIT and Signal Power

We use one vector and two pressure sensors to form a sparse large aperture L-shape array for high performance two-dimensional (2D) direction of arrival (DOA) and frequency estimation. Because the number of sensors is small and there is only one vector sensor in the presented array, thus, the installation of sensors in the array is simpler and installation error is smaller, than the conventional array. Meanwhile, a high performance 2D DOA and frequency estimation method is presented. Firstly, utilizing single vector sensor and based on the ESPRIT, a group coarse 2D DOA and frequency parameters are obtained. Secondly, to restrain space noise or interference, a matrix filter is utilized to process the covariance matrix which comes from sensor array, so as to form a new covariance matrix which possesses high signal to noise ratio. Thirdly, utilizing the new covariance matrix and based on the ESPRIT again, accurate but ambiguity angles estimates are obtained. Fourthly, one signal power estimator and one optimization method are presented to solve the angle ambiguity and frequency ambiguity problems, respectively. The proposed method gains a high performance 2D DOA and frequency estimation results. Numerical simulations are performed to verify the feasibility of the proposed method.

High-resolution IKONOS satellite imagery for normalized difference vegetative index-related assessment applied to land clearance studies

High-resolution satellite imagery permits verification of human rights land clearance violations across international borders as a result of unstable regimes or socio-economic upheaval. Without direct access to these areas to validate allegations of human rights abuse, the use of remote sensing tools, techniques, and data is extremely important. Humanitarian assessment can benefit from software-based solutions, involving radiometrically calibrated normalized difference vegetation index and temporal change imagery. We discuss the introduction of a matrix filter approach for change detection studies to help assist rapid building detection over large search areas against a bright background to evaluate internally displaced people in the 2005 Porta Farm Zimbabwe clearances. Future wide-scale near real-time space-based monitoring with a range of digital filters would be of great benefit to international human rights observers and human rights networks.