Agilent Technologies

About: Agilent Technologies is a company organization based out in Santa Clara, California, United States. It is known for research contribution in the topics: Signal & Mass spectrometry. The organization has 7398 authors who have published 11518 publications receiving 262410 citations. The organization is also known as: Agilent Technologies, Inc..

Integrated microfluidic array device

Abstract: A microfluidic component having a microfluidic channel is joined to an array component having a flexible array substrate. In an embodiment, the array component includes a prefabricated flexible array that couples with the microfluidic component in modular fashion. The modular architecture provides for different combinations of microfluidic components and array components that can be used to create customized processing and analysis tools.

Automatic image orientation detection

Abstract: We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.

Network monitoring system with built-in monitoring data gathering

Abstract: The network monitoring system comprises a network router with built-in monitoring data gathering. The network router includes channels through which data pass in packets. Each of the packets includes a packet header. The network router additionally includes a header copier and a packet generator. The header copier generates a header copy from the packet header of at least some of the packets. The packet generator receives the header copies and forms monitoring data packets from them. Each monitoring data packet additionally represents temporal data relating to the header copies included in it. A method of obtaining performance data relating to a data transmission network that includes a node passes data through the node in packets. Each of the packets includes a packet header. At least some of the packet headers are copied to obtain respective header copies as monitoring data from which monitoring data packets are formed. The monitoring data packets additionally represent temporal data relating to the header copies included in them. The monitoring data packets are transmitted and the performance data are generated from the monitoring data contained in the monitoring data packets.

Signal analysis using a multiresolution form of the singular value decomposition

Abstract: This paper proposes a multiresolution form of the singular value decomposition (SVD) and shows how it may be used for signal analysis and approximation. It is well-known that the SVD has optimal decorrelation and subrank approximation properties. The multiresolution form of SVD proposed here retains those properties, and moreover, has linear computational complexity. By using the multiresolution SVD, the following important characteristics of a signal may he measured, at each of several levels of resolution: isotropy, sphericity of principal components, self-similarity under scaling, and resolution of mean-squared error into meaningful components. Theoretical calculations are provided for simple statistical models to show what might be expected. Results are provided with real images to show the usefulness of the SVD decomposition.