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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

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The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.

ARIMA model --- time series analysis --- online optimization --- online model selection --- precipitation nowcasting --- deep learning --- autoencoders --- radar data --- generalization error --- recurrent neural networks --- machine learning --- model predictive control --- nonlinear systems --- neural networks --- low power --- quantization --- CNN architecture --- multi-objective optimization --- genetic algorithms --- evolutionary computation --- swarm intelligence --- Heating, Ventilation and Air Conditioning (HVAC) --- metaheuristics search --- bio-inspired algorithms --- smart building --- soft computing --- training --- evolution of weights --- artificial intelligence --- deep neural networks --- convolutional neural network --- deep compression --- DNN --- ReLU --- floating-point numbers --- hardware acceleration --- energy dissipation --- FLOW-3D --- hydraulic jumps --- bed roughness --- sensitivity analysis --- feature selection --- evolutionary algorithms --- nature inspired algorithms --- meta-heuristic optimization --- computational intelligence

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The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.

Research & information: general --- Mathematics & science --- ARIMA model --- time series analysis --- online optimization --- online model selection --- precipitation nowcasting --- deep learning --- autoencoders --- radar data --- generalization error --- recurrent neural networks --- machine learning --- model predictive control --- nonlinear systems --- neural networks --- low power --- quantization --- CNN architecture --- multi-objective optimization --- genetic algorithms --- evolutionary computation --- swarm intelligence --- Heating, Ventilation and Air Conditioning (HVAC) --- metaheuristics search --- bio-inspired algorithms --- smart building --- soft computing --- training --- evolution of weights --- artificial intelligence --- deep neural networks --- convolutional neural network --- deep compression --- DNN --- ReLU --- floating-point numbers --- hardware acceleration --- energy dissipation --- FLOW-3D --- hydraulic jumps --- bed roughness --- sensitivity analysis --- feature selection --- evolutionary algorithms --- nature inspired algorithms --- meta-heuristic optimization --- computational intelligence --- ARIMA model --- time series analysis --- online optimization --- online model selection --- precipitation nowcasting --- deep learning --- autoencoders --- radar data --- generalization error --- recurrent neural networks --- machine learning --- model predictive control --- nonlinear systems --- neural networks --- low power --- quantization --- CNN architecture --- multi-objective optimization --- genetic algorithms --- evolutionary computation --- swarm intelligence --- Heating, Ventilation and Air Conditioning (HVAC) --- metaheuristics search --- bio-inspired algorithms --- smart building --- soft computing --- training --- evolution of weights --- artificial intelligence --- deep neural networks --- convolutional neural network --- deep compression --- DNN --- ReLU --- floating-point numbers --- hardware acceleration --- energy dissipation --- FLOW-3D --- hydraulic jumps --- bed roughness --- sensitivity analysis --- feature selection --- evolutionary algorithms --- nature inspired algorithms --- meta-heuristic optimization --- computational intelligence

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This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections.

Technology: general issues --- History of engineering & technology --- underground robots --- automation in mines --- remote control --- autonomous underground machinery --- robotic manipulator --- identification --- calibration --- Jacobian --- workcell layout detection --- QR code detection --- adaptive thresholding --- finder pattern --- perspective transformation --- smart buildings --- shared sensory system --- surveillance system --- localization --- marker detection --- manipulation --- design --- analytical calculation --- numerical analysis --- manipulation platform --- 3D deformable object --- grasping --- robust manipulation --- robot hand --- Matlab --- CoppeliaSim Edu --- V-Rep --- SolidWorks --- kinematics --- inverse kinematics (IK) --- manufacturing technology --- modular robots --- coordinate transformation --- industrial robot --- renishaw Ballbar QC20–W --- positional analysis --- measurement --- visual semantic SLAM --- semantic segmentation --- FPGA --- hardware acceleration --- mobile robots --- automated machining --- milling strategies --- geometrical tolerances --- assisted assembly --- mixed reality --- collaborative robot --- digital twin --- convolutional neural networks --- planar robotic arm --- pneumatic artificial muscle (PAM) --- gravity test --- dynamic model --- validation --- optimization --- trust region algorithm --- chimney --- cleaning --- in-pipe robot --- pneumatic actuator --- simulation --- pre-identification --- least squares method --- instrumental variable method --- robotics --- sensor --- antagonistic system --- pneumatic artificial muscles --- function --- model --- trajectory planning method --- hexapod robots --- legged robots --- triangle gait --- automation --- drives --- actuators --- sensors --- diagnostics --- holography --- virtual instrumentation --- control systems --- robot navigation --- reverse validation --- trajectory control --- underground robots --- automation in mines --- remote control --- autonomous underground machinery --- robotic manipulator --- identification --- calibration --- Jacobian --- workcell layout detection --- QR code detection --- adaptive thresholding --- finder pattern --- perspective transformation --- smart buildings --- shared sensory system --- surveillance system --- localization --- marker detection --- manipulation --- design --- analytical calculation --- numerical analysis --- manipulation platform --- 3D deformable object --- grasping --- robust manipulation --- robot hand --- Matlab --- CoppeliaSim Edu --- V-Rep --- SolidWorks --- kinematics --- inverse kinematics (IK) --- manufacturing technology --- modular robots --- coordinate transformation --- industrial robot --- renishaw Ballbar QC20–W --- positional analysis --- measurement --- visual semantic SLAM --- semantic segmentation --- FPGA --- hardware acceleration --- mobile robots --- automated machining --- milling strategies --- geometrical tolerances --- assisted assembly --- mixed reality --- collaborative robot --- digital twin --- convolutional neural networks --- planar robotic arm --- pneumatic artificial muscle (PAM) --- gravity test --- dynamic model --- validation --- optimization --- trust region algorithm --- chimney --- cleaning --- in-pipe robot --- pneumatic actuator --- simulation --- pre-identification --- least squares method --- instrumental variable method --- robotics --- sensor --- antagonistic system --- pneumatic artificial muscles --- function --- model --- trajectory planning method --- hexapod robots --- legged robots --- triangle gait --- automation --- drives --- actuators --- sensors --- diagnostics --- holography --- virtual instrumentation --- control systems --- robot navigation --- reverse validation --- trajectory control