Geo-correction of high-resolution imagery using fast template matching on a GPU in emergency mapping contexts

The increasing availability of satellite imagery acquired from existing and new sensors allow a wide variety of new applications that depend on the use of diverse spectral and spatial resolution data sets. One of the pre-conditions for the use of hybrid image data sets is a consistent geo-correction capacity. We demonstrate how a novel fast template matching approach implemented on a Graphics Processing Unit (GPU) allows us to accurately and rapidly geo-correct imagery in an automated way. The key difference with existing geo-correction approaches, which do not use a GPU, is the possibility to match large source image segments (8192 by 8192 pixels) with relatively large templates (512 by 512 pixels). Our approach is sufficiently robust to allow for the use of various reference data sources. The need for accelerated processing is relevant in our application context, which relates to mapping activities in the European Copernicus emergency management service. Our new method is demonstrated over an area North-West of Valencia (Spain) for a large forest fire event in July 2012. We use DEIMOS-1 and RapidEye imagery for the delineation of burnt fire scar extent. Automated geo-correction of each full resolution image sets takes approximately 1 minute. The reference templates are taken from the TerraColor data set and the Spanish national ortho-imagery data base, through the use of dedicate web map services (WMS). Geo-correction results are compared to the vector sets derived in the related Copernicus emergency service activation request.JRC.G.2-Global security and crisis managemen

Biomedical Image Registration by means of Bacterial Foraging Paradigm

Image registration (IR) is the process of geometric overlaying or alignment f two or more 2D/3D images of the same scene (unimodal registration), taken r not at different time slots, from different angles, and/or by different image acquisition ystems (multimodal registration). Technically, image registration implies  complex optimization of different parameters, performed at local or/and global evel. Local optimization methods often fail because functions of the involved metrics ith respect to transformation parameters are generally nonconvex and irregular, and lobal methods are required, at least at the beginning of the procedure. This paper resents a new evolutionary and bio-inspired robust approach for IR, Bacterial Foraging ptimization Algorithm (BFOA), which is adapted for PET-CT multimodal nd magnetic resonance image rigid registration. Results of optimizing the normalized utual information and normalized cross correlation similarity metrics validated he efficacy and precision of the proposed method by using a freely available medical mage database

Real-time Intrusion Detection using Multidimensional Sequence-to-Sequence Machine Learning and Adaptive Stream Processing

A network intrusion is any unauthorized activity on a computer network. There are host-based and network-based Intrusion Detection Systems (IDS\u27s), of which there are each signature-based and anomaly-based detection methods. An anomalous network behavior can be defined as an intentional violation of the expected sequence of packets. In a real-time network-based IDS, incoming packets are treated as a stream of data. A stream processor takes any stream of data or events and extracts interesting patterns on the fly. This representation allows applying statistical anomaly detection using sequence prediction algorithms as well as using a stream processor to perform signature-based intrusion detection and sequence extraction from a stream of packets. In this thesis, a Multidimensional Sequence to Multidimensional Sequence (MSeq2MSeq) encoder-decoder model is proposed to predict sequences of packets and an adaptive and functionally auto-scaling stream processor: Wisdom is proposed to process streams of packets. The proposed MSeq2MSeq model trained on legitimate traffic is able to detect Neptune Denial of Service (DoS) attacks, and Port Scan probes with 100% detection rate using the DARPA 1999 dataset. A hybrid algorithm using Particle Swarm Optimization (PSO) and Bisection algorithms was developed to optimize Complex Event Processing (CEP) rules in Wisdom . Adaptive CEP rules optimized by the above algorithm was able to detect FTP Brute Force attack, Slow Header DoS attack, and Port Scan probe with 100% detection rate while processing over 2.5 million events per second. An adaptive and functionally auto-scaling IDS was built using the MSeq2MSeq model and Wisdom stream processor to detect and prevent attacks based on anomalies and signature in real-time. The proposed IDS adapts itself to obtain best results without human intervention and utilizes available system resources in functionally auto-scaling deployment. Results show that the proposed IDS detects FTP Brute Force attack, Slow Header DoS attack, HTTP Unbearable Load King (HULK) DoS attack, SQL Injection attack, Web Brute Force attack, Cross-site scripting attack, Ares Botnet attack, and Port Scan probe with a 100% detection rate in a real-time environment simulated from the CICIDS 2017 dataset

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Motion correction of PET/CT images

Indiana University-Purdue University Indianapolis (IUPUI)The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses. In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

SCALABLE TECHNIQUES FOR SCHEDULING AND MAPPING DSP APPLICATIONS ONTO EMBEDDED MULTIPROCESSOR PLATFORMS

A variety of multiprocessor architectures has proliferated even for off-the-shelf computing platforms. To make use of these platforms, traditional implementation frameworks focus on implementing Digital Signal Processing (DSP) applications using special platform features to achieve high performance. However, due to the fast evolution of the underlying architectures, solution redevelopment is error prone and re-usability of existing solutions and libraries is limited. In this thesis, we facilitate an efficient migration of DSP systems to multiprocessor platforms while systematically leveraging previous investment in optimized library kernels using dataflow design frameworks. We make these library elements, which are typically tailored to specialized architectures, more amenable to extensive analysis and optimization using an efficient and systematic process. In this thesis we provide techniques to allow such migration through four basic contributions: 1. We propose and develop a framework to explore efficient utilization of Single Instruction Multiple Data (SIMD) cores and accelerators available in heterogeneous multiprocessor platforms consisting of General Purpose Processors (GPPs) and Graphics Processing Units (GPUs). We also propose new scheduling techniques by applying extensive block processing in conjunction with appropriate task mapping and task ordering methods that match efficiently with the underlying architecture. The approach gives the developer the ability to prototype a GPU-accelerated application and explore its design space efficiently and effectively. 2. We introduce the concept of Partial Expansion Graphs (PEGs) as an implementation model and associated class of scheduling strategies. PEGs are designed to help realize DSP systems in terms of forms and granularities of parallelism that are well matched to the given applications and targeted platforms. PEGs also facilitate derivation of both static and dynamic scheduling techniques, depending on the amount of variability in task execution times and other operating conditions. We show how to implement efficient PEG-based scheduling methods using real time operating systems, and to re-use pre-optimized libraries of DSP components within such implementations. 3. We develop new algorithms for scheduling and mapping systems implemented using PEGs. Collectively, these algorithms operate in three steps. First, the amount of data parallelism in the application graph is tuned systematically over many iterations to profit from the available cores in the target platform. Then a mapping algorithm that uses graph analysis is developed to distribute data and task parallel instances over different cores while trying to balance the load of all processing units to make use of pipeline parallelism. Finally, we use a novel technique for performance evaluation by implementing the scheduler and a customizable solution on the programmable platform. This allows accurate fitness functions to be measured and used to drive runtime adaptation of schedules. 4. In addition to providing scheduling techniques for the mentioned applications and platforms, we also show how to integrate the resulting solution in the underlying environment. This is achieved by leveraging existing libraries and applying the GPP-GPU scheduling framework to augment a popular existing Software Defined Radio (SDR) development environment -- GNU Radio -- with a dataflow foundation and a stand-alone GPU-accelerated library. We also show how to realize the PEG model on real time operating system libraries, such as the Texas Instruments DSP/BIOS. A code generator that accepts a manual system designer solution as well as automatically configured solutions is provided to complete the design flow starting from application model to running system