Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Long description: The aim of this workshop is to make FPGA and reconfigurable technology accessible to software programmers.Despite their frequently proven power and performance benefits, designing for FPGAs is mostly an engineering discipline carried out by highly trained specialists.With recent progress in high-level synthesis, a first important step towards bringing FPGA technology to potentially millions of software developers was taken. The FSP Workshop aims at bringing researchers and experts from both academia and industry together to discuss and exchange the latest research advances and future trends.This includes high-level compilation and languages, design automation tools that raise the abstraction level when designing for (heterogeneous) FPGAs and reconfigurable systems and standardized targetplatforms.This will in particular put focus on the requirements of software developers and application engineers.In addition, a distinctive feature of the workshop will be its cross section through all design levels, ranging from programming down to custom hardware.Thus, the workshop is targeting all those who are interested in understanding the big picture and the potential of domain-specific computing and software-driven FPGA development.In addition, the FSP Workshop shall facilitate collaboration of the different domains. Topics of the FSP Workshop include, but are not limited to: • High-level synthesis (HLS) and domain-specific languages (DSLs) for FPGAs and heterogeneous systems • Mapping approaches and tools for heterogeneous FPGAs • Support of hard IP blocks such as embedded processors and memory interfaces • Development environments for software engineers (automated tool flows, design frameworks and tools, tool interaction) • FPGA virtualization (design for portability, resource sharing, hardware abstraction) • Design automation technologies for multi-FPGA and heterogeneous systems • Methods for leveraging (partial) dynamic reconfiguration to increase performance, flexibility, reliability, or programmability • Operating system services for FPGA resource management, reliability, security • Target hardware design platforms (infrastructure, drivers, portable systems) • Overlays (CGRAs, vector processors, ASIP- and GPU-like intermediate fabrics) • Applications (e.g., embedded computing, signal processing, bio informatics, big data,database acceleration) using C/C++/SystemC-based HLS, OpenCL, OpenSPL, etc. • Directions for collaborations (research proposals, networking, Horizon 2020)

FPGA --- Design Automation --- Hardware Acceleration --- High-Level Synthesis --- Reconfigurable Computing --- Software-Driven FPGA Developm. --- System-on-Chip

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs.

nuclei detection --- System-on-Chip --- FPGA --- K-Means --- hardware acceleration --- image analysis --- perceptual coding --- line buffer --- heterogeneous computing --- window filters --- processor architectures --- hardware accelerators --- stream processing --- embedded systems --- image processing pipeline --- image processing --- generalized Laplacian of Gaussian filter --- background estimation --- real-time systems --- FPGA implementation --- hardware architecture --- compression --- image borders --- memory --- zig-zag scan --- histopathology --- just-noticeable difference (JND) --- memory management --- downsampling --- image segmentation --- feature extraction --- design --- mean Shift clustering --- high-throughput --- segmentation --- streaming architecture --- power --- D-SWIM --- hardware/software co-design --- high-level synthesis --- contrast masking --- texture detection --- pipeline --- field programmable gate array (FPGA) --- JPEG-LS --- low-latency --- connected components analysis --- luminance masking --- field programmable gate arrays (FPGA)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.

Information technology industries --- high-level synthesis --- HLS --- SDSoC --- support vector machines --- SVM --- code refactoring --- Zynq --- ZedBoard --- extreme edge --- embedded edge computing --- internet of things deployment --- hardware design --- IoT security --- Contiki-NG --- trustability --- embedded systems --- collaborative filtering --- recommender systems --- parallelism --- reconfigurable hardware --- neuroevolution --- block-based neural network --- dynamic and partial reconfiguration --- scalability --- reinforcement learning --- embedded system --- artificial intelligence --- hardware acceleration --- neuromorphic processor --- power consumption --- harsh environment --- fog computing --- edge computing --- cloud computing --- IoT gateway --- LoRa --- WiFi --- low power consumption --- low latency --- flexible --- smart port --- quantisation --- evolutionary algorithm --- neural network --- FPGA --- Movidius VPU --- 2D graphics accelerator --- line-drawing --- Bresenham’s algorithm --- alpha-blending --- anti-aliasing --- field-programmable gate array --- deep learning --- performance estimation --- Gaussian process --- high-level synthesis --- HLS --- SDSoC --- support vector machines --- SVM --- code refactoring --- Zynq --- ZedBoard --- extreme edge --- embedded edge computing --- internet of things deployment --- hardware design --- IoT security --- Contiki-NG --- trustability --- embedded systems --- collaborative filtering --- recommender systems --- parallelism --- reconfigurable hardware --- neuroevolution --- block-based neural network --- dynamic and partial reconfiguration --- scalability --- reinforcement learning --- embedded system --- artificial intelligence --- hardware acceleration --- neuromorphic processor --- power consumption --- harsh environment --- fog computing --- edge computing --- cloud computing --- IoT gateway --- LoRa --- WiFi --- low power consumption --- low latency --- flexible --- smart port --- quantisation --- evolutionary algorithm --- neural network --- FPGA --- Movidius VPU --- 2D graphics accelerator --- line-drawing --- Bresenham’s algorithm --- alpha-blending --- anti-aliasing --- field-programmable gate array --- deep learning --- performance estimation --- Gaussian process

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis.

deep learning --- segmentation --- prostate --- MRI --- ENet --- UNet --- ERFNet --- radiomics --- gamma knife --- imaging quantification --- [11C]-methionine positron emission tomography --- cancer --- atrial fibrillation --- 4D-flow --- stasis --- pulmonary vein ablation --- convolutional neural network --- transfer learning --- maxillofacial fractures --- computed tomography images --- radiography --- xenotransplant --- cancer cells --- zebrafish image analysis --- in vivo assay --- convolutional neural network (CNN) --- magnetic resonance imaging (MRI) --- neoadjuvant chemoradiation therapy (nCRT) --- pathologic complete response (pCR) --- rectal cancer --- radiomics feature robustness --- PET/MRI co-registration --- image registration --- fundus image --- feature extraction --- glomerular filtration rate --- Gate’s method --- renal depth --- computed tomography --- computer-aided diagnosis --- medical-image analysis --- automated prostate-volume estimation --- abdominal ultrasound images --- image-patch voting --- soft tissue sarcoma --- volume estimation --- artificial intelligence --- Basal Cell Carcinoma --- skin lesion --- classification --- colon --- positron emission tomography-computed tomography --- nuclear medicine --- image pre-processing --- high-level synthesis --- X-ray pre-processing --- pipelined architecture --- n/a --- Gate's method

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

Technology: general issues --- History of engineering & technology --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging