Coherent noise source identification in multi channel analysis

The evaluation of coherent noise can provide useful information in the study of detectors. The identification of coherent noise sources is also relevant for uncertainty calculations in analyse where several channels are combined. The study of the covariance matrix give information about coherent noises. Since covariance matrix of high dimension data could be difficult to analyse, the development of analysis tools is needed. Principal Component Analysis (PCA) is a powerful tool for such analysis. It has been shown that we can use PCA to find coherent noises in ATLAS calorimeter or the CALICE Si-W electromagnetic calorimeter physics prototype. However, if several coherent noise sources are combined, the interpretation of the PCA may become complicated. In this paper, we present another method based on the study of the covariance matrix to identify noise sources. This method has been developed for the study of front end ASICs dedicated to CALICE calorimeters. These calorimeters are designed and studied for experiments at the ILC. We also study the reliability of the method with simulations. Although this method has been developped for a specific application, it can be used for any multi channel analysis.Comment: Public version of the CALICE Internal Note CIN-02

Identification-method research for open-source software ecosystems

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework

Inverse Source Identification based on Acoustic Particle Velocity Measurements

A general applicable acoustic source identification method is the inverse frequency response function technique (IFRF). In the standard IFRF method acoustic pressures measured on a grid in the nearfield of the acoustic source are used. To relate the measured field pressures to the normal velocities on the surface of the source, a transfer matrix is calculated with a boundary element method. The resulting system of equations is ill-conditioned and can only be solved by applying regularization techniques. In this paper, it is described how the nearfield particle velocities can be used instead of pressures to reconstruct the original source vibrations. By means of a simulated experiment, a comparison is made between pressure based and velocity based IFRF

Air pollution source identification

The techniques available for source identification are reviewed: remote sensing, injected tracers, and pollutants themselves as tracers. The use of the large number of trace elements in the ambient airborne particulate matter as a practical means of identifying sources is discussed. Trace constituents are determined by sensitive, inexpensive, nondestructive, multielement analytical methods such as instrumental neutron activation and charged particle X-ray fluorescence. The application to a large data set of pairwise correlation, the more advanced pattern recognition-cluster analysis approach with and without training sets, enrichment factors, and pollutant concentration rose displays for each element is described. It is shown that elemental constituents are related to specific source types: earth crustal, automotive, metallurgical, and more specific industries. A field-ready source identification system based on time and wind direction resolved sampling is described

Writer Identification Using Inexpensive Signal Processing Techniques

We propose to use novel and classical audio and text signal-processing and otherwise techniques for "inexpensive" fast writer identification tasks of scanned hand-written documents "visually". The "inexpensive" refers to the efficiency of the identification process in terms of CPU cycles while preserving decent accuracy for preliminary identification. This is a comparative study of multiple algorithm combinations in a pattern recognition pipeline implemented in Java around an open-source Modular Audio Recognition Framework (MARF) that can do a lot more beyond audio. We present our preliminary experimental findings in such an identification task. We simulate "visual" identification by "looking" at the hand-written document as a whole rather than trying to extract fine-grained features out of it prior classification.Comment: 9 pages; 1 figure; presented at CISSE'09 at http://conference.cisse2009.org/proceedings.aspx ; includes the the application source code; based on MARF described in arXiv:0905.123

Chandra localization of XTE J1906+090 and discovery of its optical and infrared counterparts

We present the Chandra identification and localization of the transient X-ray source XTE J1906+090 and the discovery of its optical and infrared counterparts. Our analysis of archival Chandra ACIS-I observations of the field found the source approximately 8 away from the position determined earlier with the RXTE PCA. We have confirmed the source identification with timing analysis of the X-ray data, which detected the source spin period of 89.6 s. The best Chandra position for the source is R.A. = 19h04m47491, decl. = +09024140. Subsequently, we performed optical observations of the field around the new location and discovered a coincident optical source with R-band magnitude of 18.7. A search in the Two Micron All Sky Survey catalog revealed an infrared point source with J = 15.2, H = 14.2, and K = 13.5, whose location is also coincident with our Chandra and optical positions. Our results add fresh evidence for a Be/X-ray transient nature for XTE J1906+090