A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Parallel graphics and visualization

Computer Graphics and Visualization are two fields that continue to evolve at a fast pace, always addressing new application areas and achieving better and faster results. The volume of data processed by such applications keeps getting larger and the illumination and light transport models used to generate pictorial representations of this data keep getting more sophisticated. Richer illumination and light transport models allow the generation of richer images that convey more information about the phenomenons or virtual worlds represented by the data and are more realistic and engaging to the user. The combination of large data sets, rich illumination models and large, sophisticated displays results in huge workloads that cannot be processed sequentially and still maintain acceptable response times. Parallel processing is thus an obvious approach to such problems, creating the field of Parallel Graphics and Visualization. The Eurographics Symposium on Parallel Graphics and Visualization (EGPGV) gathers together researchers from all over the world to foster research focused on theoretical and applied issues critical to parallel and distributed computing and its application to all aspects of computer graphics, virtual reality, scientific and engineering visualization. This special issue is a collection of five papers selected from those presented at the 7th EGPGV, which took place in Lugano, Switzerland, in May, 2007. The research presented in this symposium has evolved over the years, often reflecting the evolution of the underlying systems’ architectures. While papers presented in the first few events focused on Single Instruction Multiple Data and Massively Parallel Multi-Processing systems, in recent years the focus was mainly on Symmetric Multiprocessing machines and PC clusters, often also including the utilization of multiple Graphics Processing Units. The 2007 event witnessed the first papers addressing multicore processors, thus following the general trend of computer systems’ architecture. The paper by Wald, Ize and Parker discusses acceleration structures for interactive ray tracing of dynamic scenes. They propose the utilization of Bounding Volume Hierarchies (BVH), which for deformable scenes can be rapidly updated by adjusting the bounding primitives while maintaining the hierarchy. To avoid a significant performance penalty due to a large mismatch between the scene geometry and the tree topology the BVH is rebuilt asynchronously and concurrently with rendering. According to the authors, in the near future interactive ray tracers are expected to run on highly parallel multicore architectures. Thus, all results reported were obtained on an 8 processor dual core system, totalling 16 cores. Gribble, Brownlee and Parker propose two algorithms targeting highly parallel multicore architectures enabling interactive navigation and exploration of large particle data sets with global illumination effects. Rendering samples are lazily evaluated using Monte Carlo path tracing, while visualization occurs asynchronously by using Dynamic Luminance Textures that cache the renderer results. The combined utilization of particle based simulation methods and global illumination enables the effective communication of subtle changes in the three-dimensional structure of the data. All results were also obtained on a 16 cores architecture. The paper by Thomaszweski, Pabst and Blochinger analyzes parallel techniques for physically based simulation, in particular, the time integration and collision handling phases. The former is addressed using the conjugate gradient algorithm and static problem decomposition, while the latter exhibits a dynamic structure, thus requiring fully dynamic task decomposition. Their results were obtained using three different quad-core systems. Hong and Shen derive an efficient parallel algorithm for symmetry computation in volume data represented by regular grids. Sequential detection of symmetric features in volumetric data sets has a prohibitive cost, thus requiring efficient parallel algorithms and powerful parallel systems. The authors obtained the reported results on a PC cluster with Infiniband and 64 nodes, each being a dual processor, single core Opteron. Bettio, Gobbetti, Marton and Pintore describe a scalable multiresolution rendering system targeting massive triangle meshes and driving different sized light field displays. The larger light field display ð1:6 0:9m2Þ is based on a special arrangement of projectors and a holographic screen. It allows multiple freely moving viewers to see the scene from their respective points of view and enjoy continuous horizontal parallax without any specialized viewing devices. To drive this 35 Mbeams display they use a scalable parallel renderer, resorting to out of core and level of detail techniques, and running on a 15 nodes PC cluster

A low-complexity turbo decoder architecture for energy-efficient wireless sensor networks

Turbo codes have recently been considered for energy-constrained wireless communication applications, since they facilitate a low transmission energy consumption. However, in order to reduce the overall energy consumption, Look-Up- Table-Log-BCJR (LUT-Log-BCJR) architectures having a low processing energy consumption are required. In this paper, we decompose the LUT-Log-BCJR architecture into its most fundamental Add Compare Select (ACS) operations and perform them using a novel low-complexity ACS unit. We demonstrate that our architecture employs an order of magnitude fewer gates than the most recent LUT-Log-BCJR architectures, facilitating a 71% energy consumption reduction. Compared to state-of- the-art Maximum Logarithmic Bahl-Cocke-Jelinek-Raviv (Max- Log-BCJR) implementations, our approach facilitates a 10% reduction in the overall energy consumption at ranges above 58 m