Parallel Algorithms for Constrained Tensor Factorization via the Alternating Direction Method of Multipliers

Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as nonnegativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation on regular high-performance computing (e.g., mesh) architectures. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using nonnegativity as a baseline constraint, but the proposed framework can more-or-less readily incorporate many other types of constraints. Numerical experiments are very encouraging, indicating that the ADMoM-based nonnegative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.Comment: Submitted to the IEEE Transactions on Signal Processin

PDA-based system for monitoring electromagnetic signals

The development of a mobile system for receiving, storing, and displaying electromagnetic-signals (EM) at specific frequencies using mobile devices and wireless networks, is of extreme interest, especially when the final means of display is a PDA, a very light and compact handheld device. In the present study, an application is developed for remote monitoring of EM-signals preceding seismic events. The particular advantages and challenges faced when developing such application are explained and future work in this area is presented

On the status of orbital high-resolution repeat imaging of Mars for the observation of dynamic surface processes

This work deals with the meta-data analysis of high-resolution orbital imagery that was acquired over the last four decades of Mars. The objective of this analysis is to provide a starting point for planetary scientists who are interested in examining the martian surface in order to detect changes that are related to not fully understood natural phenomena. An image aggregation method is introduced and used to generate image groupings related to prioritising regions for change detection. The parameters determining each grouping are the season, the Martian Year and the local time that an image was acquired, the imaging instrument and its resolution. The analysis shows that there is sufficient coverage to systematically examine periodic martian phenomena in images that depict the same area over the same season, as well as sporadic martian phenomena (e.g. a new crater) in images that depict the same area in different time periods. The end product of this work is a series of 35 global coverage maps demonstrating the high-resolution repeat coverage of Mars up to Martian Year 31 under different temporal and viewing condition constraints. These are available both through supplementary material as well as via a web-GIS

A Systematic Solution to Multi-Instrument Coregistration of High-Resolution Planetary Images to an Orthorectified Baseline

We address the problem of automatically coregistering planetary images to a common baseline, introducing a novel generic technique that achieves an unprecedented robustness to different image inputs, thus making batch-mode coregistration achievable without requiring the usual parameter tweaking. We introduce a novel image matching technique, which boosts matching performance even under the most strenuous circumstances, and experimentally demonstrate validation through an extensive experimental multi-instrument setup that includes images from eight high-resolution data sets of the Mars and the Moon. The technique is further tested in a batch-mode processing, in which approximately 1.6% of all high-resolution Martian imagery is coregistered to a common baseline

A search for polycyclic aromatic hydrocarbons over the Martian South Polar Residual Cap

We present our research on compositional mapping of the Martian South Polar Residual Cap (SPRC), especially the detection of organic signatures within the dust content of the ice, based on hyperspectral data analysis. The SPRC is the main region of interest for this investigation, because of the unique CO 2 ice sublimation features that cover the surface. These flat floored, circular depressions are highly dynamic, and we infer frequently expose dust particle s previously trapped within the ice during the wintertime. Here we identify suitable regions for potential dust exposure on the SPRC, and utilise data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board NASA's Mars Reconnaissance Orbiter (MRO) satellite to examine infrared spectra of dark regions assumed to be composed mainly of dust particles to establish their mineral composition, to eliminate the effects of ices on sub-pixel dusty features, and to look for signatures indicative of Polycyclic Aromatic Hydrocarbons (PAHs). Spectral mapping has identified compositional differences between depression rims and the majority of the SPRC and CRISM spectra have been corrected to minimise the influence of CO 2 ice. Whilst no conclusive evidence for PAHs has been found within the detectability limits of the CRISM instrument, depression rims are shown to have higher water content than regions of featureless ice, and there are possible indications of magnesium carbonate within the dark, dusty regions

A smartphone-based Teleradiology system

The development of a teleradiology application for remote monitoring and processing of patient image data using 2nd generation mobile devices with enhanced network services, is of extreme interest, especially when the final means of display is a smartphone, a very light and compact handheld device. In the following paper the development of applications, that are responsible for remote monitoring and processing of medical images, is investigated

CloudFCN: Accurate and robust cloud detection for satellite imagery with deep learning

Cloud masking is of central importance to the Earth Observation community. This paper deals with the problem of detecting clouds in visible and multispectral imagery from high-resolution satellite cameras. Recently, Machine Learning has offered promising solutions to the problem of cloud masking, allowing for more flexibility than traditional thresholding techniques, which are restricted to instruments with the requisite spectral bands. However, few studies use multi-scale features (as in, a combination of pixel-level and spatial) whilst also offering compelling experimental evidence for real-world performance. Therefore, we introduce CloudFCN, based on a Fully Convolutional Network architecture, known as U-net, which has become a standard Deep Learning approach to image segmentation. It fuses the shallowest and deepest layers of the network, thus routing low-level visible content to its deepest layers. We offer an extensive range of experiments on this, including data from two high-resolution sensors-Carbonite-2 and Landsat 8-and several complementary tests. Owing to a variety of performance-enhancing design choices and training techniques, it exhibits state-of-the-art performance where comparable to other methods, high speed, and robustness to many different terrains and sensor types

A compact plug-in module for LHC-like trigger emulation

A compact trigger emulation module for evaluating electronic systems for LHC applications has been built using off-the-shelf components. The emulator, which is based on an FPGA, generates both programmable and true-random trigger patterns in compliance with the LHC triggering rules. For the true-random trigger part, the source of randomness is the avalanche effect on a transistor emitter-base diode. The system can be used either as a plug-in module for VME systems or as a standalone device controlled via a standard USB link by a PC running LabVIEW