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Methods based on order statistics are often used in finance, quality control, data and signal processing, especially when signals of interest are immersed in impulsive noise. These allow to include rank information by increasing the dimension of the problem. In large dimension problems, we are usually required to know only the second order statistics. In this article we use a rank-one quadratic measurement...
Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified...
In this paper, we propose a cross-modal deep variational hashing (CMDVH) method for cross-modality multimedia retrieval. Unlike existing cross-modal hashing methods which learn a single pair of projections to map each example as a binary vector, we design a couple of deep neural network to learn non-linear transformations from image-text input pairs, so that unified binary codes can be obtained. We...
PLS is widely used in the quality control process system, but it has poor capability in some strong local nonlinear system for fault diagnosis. To enhance the monitoring ability of such type fault, a novel statistical model based on global plus local projection to latent structures (GPLPLS) is proposed. Firstly, the characteristics and nature of quality-related global and local partial least squares...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
Image match has been widely used in computer vision, pattern recognition and image processing. The matching efficiency is a focus topic in the field and some methods have been presented, such as simplification of similarity measure, application of optimization algorithms. Particle swarm optimization algorithm (PSO) has been utilized successfully for image match. However, it is easy to fall into the...
Subharmonic aided pressure estimation (SHAPE) uses ultrasound contrast agents (UCAs) to estimate hydrostatic pressure by transmitting at one frequency, receiving at its subharmonic frequency and then monitoring the subharmonic amplitude variations. The subharmonic response of the UCAs has an inverse linear relationship with the ambient pressure. In order to optimize SHAPE, we studied the impact of...
In the article information technologies of optimization of structures of the technical systems are examined at numerical bundles as comfortable mathematical models with a non-uniform structure on the example of development of models of quasibarker and pseudocasual sequences, development of optimal charts of renewal for the up-diffused calculations, development of effective filters of images, development...
This paper considers the problem of forming formal statements regarding the formal parameters of binary code sequences, mathematical model transformations, their properties and the order of operations while optimizing the sequence on the criteria of minimal side lobes. The results of modeling are shown as a group of optimized binary code sequences.
Plane wave methods for ultrafast ultrasound imaging suffer from a low signal to noise ratio (SNR) and a limited field of view at greater imaging depths. Imaging using multiple focused coded beams in parallel is one strategy for high speed imaging that may improve on these limitations. However, the SNR and resolution of this strategy are degraded by the interference between the beams transmitted in...
Multipath is known to be one of the dominant error sources in high accuracy positioning systems, and multipath estimation is crucial for multipath mitigation. Most existing multipath estimation algorithms usually consider the cases of single mutlipath with Gaussian noise. However, non-Gaussian noises and two-multipath are often encountered in many practical environments. In this paper, a new algorithm...
Blind signal extraction is particularly attractive to solve signal mixture problems while only one or a few source signals are desired. Many desired biomedical signals exhibit distinct periods. A sequential method based on second order statistics is introduced in this paper. One can choose to recover one source signal or all signals in a specific order. The validity and performance of the proposed...
Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athlete's abilities, health, mental preparations...
We study the problem of interference mitigation in a phased array, where a subset containing k out of a total of N receivers creates a virtual spatial null for an incoming interference. The signal-of-interest and interference are represented by their corresponding steering vectors, and an optimum subarray is chosen such that the two vectors are as orthogonal as possible. This optimization is a binary...
In this paper the long standing major challenge of designing binary sequences with good (aperiodic) autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL) is considered. The problem is formulated as a bi-objective Pareto optimization forcing the binary constraint at the design stage. An iterative novel FFT-based approach exploiting the coordinate descent...
The performance of a rotating stirrer made of helically positioned paddles is experimentally analyzed. The statistically independent positions are computed as a function of the number of paddles, paddle separation and relative tilt angle. With respect to a traditional single paddle wheel stirrer, the structure combines a helical path and just a simple engine required for the rotation. The analysis...
In job-centric monitoring, monitors gather series of measurements, e.g., the used CPU load, per job. In domains where jobs are expected to behave similar, job-centric monitoring allows detecting misbehaving jobs based on a reference series of measurements. However, current detection approaches neglect time-drifts in series, e.g., caused by different CPU speeds and therefore potentially cause false...
Event correlation is the task of detecting dependencies between events in event sequences, e.g., for predictive maintenance based on log-files. In this work, a new data-driven, generic framework for event correlation is presented. First, we use a fast preliminary test statistic to determine candidate event type pairs. Next, the precise distribution of the time lag between those pairs is calculated...
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces...
Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data. Particularly, ranking-based hashing techniques have recently attracted broad research attention because ranking accuracy among the retrieved data is well explored and their objective is more applicable to realistic search tasks. However, directly optimizing discrete hash codes without...
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