Application and comparison of three tomographic techniques for detection of decay in trees

This paper reports application of electric, ultrasonic, and georadar tomography for detection of decay in trees and their comparison with the traditional penetrometer. Their feasibility in arboriculture is also evaluated, critically considering some "open problems." The experiments were carried out in an urban environment on two plane (Platanus hybrida Brot.) trees. Both trees, after felling, showed extensive white rot in the central cylinder. The electric tomography revealed low resistivity zones roughly centered in the trunk. A comparison with the successively cut sections showed a fine correspondence to decayed areas and a strong correspondence between high moisture zones and low resistivity zones. Ultrasonic tomography demonstrated to be a very effective tool for the detection of internal decay, accurately locating the position of the anomalies and estimating their size, shape, and characteristic in terms of mechanical properties. With the georadar technique, the high contrast of electromagnetic impedance measured between the inner decayed section and the outside sound section allowed the detection of the interface between the sound and decayed section of the tree, using radar acquisition in reflection modality. The penetrometer profiles detected the low-resistance areas inside the two trunk

Code Flows: Visualizing Structural Evolution of Source Code

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid-level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real-world C++ code system.

Simple to Complex Cross-modal Learning to Rank

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin

Code Flows: Visualizing Structural Evolution of Source Code

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid-level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real-world C++ code system.

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Real-time programmable acoustooptic synthetic aperture radar processor

The acoustooptic time-and-space integrating approach to real-time synthetic aperture radar (SAR) processing is reviewed, and novel hybrid optical/electronic techniques, which generalize the basic architecture, are described. The generalized architecture is programmable and has the ability to compensate continuously for range migration changes in the parameters of the radar/target geometry and anomalous platform motion. The new architecture is applicable to the spotlight mode of SAR, particularly for applications in which real-time onboard processing is required

Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise

We address the problem of adaptive detection of a signal of interest embedded in colored noise modeled in terms of a compound-Gaussian process. The covariance matrices of the primary and the secondary data share a common structure while having different power levels. A Bayesian approach is proposed here, where both the power levels and the structure are assumed to be random, with some appropriate distributions. Within this framework we propose MMSE and MAP estimators of the covariance structure and their application to adaptive detection using the NMF test statistic and an optimized GLRT herein derived. Some results, also conducted in comparison with existing algorithms, are presented to illustrate the performances of the proposed algorithms. The relevant result is that the solutions presented herein allows to improve the performance over conventional ones, especially in presence of a small number of training data