Choose an application

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

Performance evaluation is at the foundation of computer architecture research and development. Contemporary microprocessors are so complex that architects cannot design systems based on intuition and simple models only. Adequate performance evaluation methods are absolutely crucial to steer the research and development process in the right direction. However, rigorous performance evaluation is non-trivial as there are multiple aspects to performance evaluation, such as picking workloads, selecting an appropriate modeling or simulation approach, running the model and interpreting the results using meaningful metrics. Each of these aspects is equally important and a performance evaluation method that lacks rigor in any of these crucial aspects may lead to inaccurate performance data and may drive research and development in a wrong direction. The goal of this book is to present an overview of the current state-of-the-art in computer architecture performance evaluation, with a special emphasis on methods for exploring processor architectures. The book focuses on fundamental concepts and ideas for obtaining accurate performance data. The book covers various topics in performance evaluation, ranging from performance metrics, to workload selection, to various modeling approaches including mechanistic and empirical modeling. And because simulation is by far the most prevalent modeling technique, more than half the book's content is devoted to simulation. The book provides an overview of the simulation techniques in the computer designer's toolbox, followed by various simulation acceleration techniques including sampled simulation, statistical simulation, parallel simulation and hardware-accelerated simulation.

Computer architecture -- Evaluation. --- Computer architecture. --- Computer simulation. --- Engineering & Applied Sciences --- Computer Science --- Computer architecture --- Electronic digital computers --- Evaluation. --- Architecture, Computer --- Performance evaluation --- Performance metrics --- Workload characterization --- Analytical modeling --- Architectural simulation --- Sampled simulation --- Statistical simulation --- Parallel simulation --- FPGA-accelerated simulation --- Mathematical models.

Choose an application

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

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

Medicine --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a

Choose an application

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

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

Medicine --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method

Choose an application

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

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot.

Technology: general issues --- Energy industries & utilities --- dynamic objects identification and localization --- laser cluster --- radial velocity similarity --- Pearson correlation coefficient --- particle filter --- trilateral indoor positioning --- RSSI filter --- RSSI classification --- stability --- accuracy --- inertial navigation system --- artificial neural network --- motion tracking --- sensor fusion --- indoor navigation system --- indoor positioning --- indoor navigation --- radiating cable --- leaky feeder --- augmented reality --- Bluetooth --- indoor positioning system --- smart hospital --- indoor --- positioning --- visually impaired --- deep learning --- multi-layered perceptron --- inertial sensor --- smartphone --- multi-variational message passing (M-VMP) --- factor graph (FG) --- second-order Taylor expansion --- cooperative localization --- joint estimation of position and clock --- RTLS --- indoor positioning system (IPS) --- position data --- industry 4.0 --- traceability --- product tracking --- fingerprinting localization --- Bluetooth low energy --- Wi-Fi --- performance metrics --- positioning algorithms --- location source optimization --- fuzzy comprehensive evaluation --- DCPCRLB --- UAV --- unmanned aerial vehicles --- NWPS --- indoor positioning systems --- GPS denied --- GNSS denied --- autonomous vehicles --- visible light positioning --- mobile robot --- calibration --- appearance-based localization --- computer vision --- Gaussian processes --- manifold learning --- robot vision systems --- image manifold --- descriptor manifold --- indoor fingerprinting localization --- Gaussian filter --- Kalman filter --- received signal strength indicator --- channel state information --- indoor localization --- visual-inertial SLAM --- constrained optimization --- path loss model --- particle swarm optimization --- beacon --- absolute position system --- cooperative algorithm --- intercepting vehicles --- robot framework --- UWB sensors --- Internet of Things (IoT) --- wireless sensor network (WSN) --- switched-beam antenna --- electronically steerable parasitic array radiator (ESPAR) antenna --- received signal strength (RSS) --- fingerprinting --- down-conversion --- GPS --- navigation --- RF repeaters --- up-conversion --- dynamic objects identification and localization --- laser cluster --- radial velocity similarity --- Pearson correlation coefficient --- particle filter --- trilateral indoor positioning --- RSSI filter --- RSSI classification --- stability --- accuracy --- inertial navigation system --- artificial neural network --- motion tracking --- sensor fusion --- indoor navigation system --- indoor positioning --- indoor navigation --- radiating cable --- leaky feeder --- augmented reality --- Bluetooth --- indoor positioning system --- smart hospital --- indoor --- positioning --- visually impaired --- deep learning --- multi-layered perceptron --- inertial sensor --- smartphone --- multi-variational message passing (M-VMP) --- factor graph (FG) --- second-order Taylor expansion --- cooperative localization --- joint estimation of position and clock --- RTLS --- indoor positioning system (IPS) --- position data --- industry 4.0 --- traceability --- product tracking --- fingerprinting localization --- Bluetooth low energy --- Wi-Fi --- performance metrics --- positioning algorithms --- location source optimization --- fuzzy comprehensive evaluation --- DCPCRLB --- UAV --- unmanned aerial vehicles --- NWPS --- indoor positioning systems --- GPS denied --- GNSS denied --- autonomous vehicles --- visible light positioning --- mobile robot --- calibration --- appearance-based localization --- computer vision --- Gaussian processes --- manifold learning --- robot vision systems --- image manifold --- descriptor manifold --- indoor fingerprinting localization --- Gaussian filter --- Kalman filter --- received signal strength indicator --- channel state information --- indoor localization --- visual-inertial SLAM --- constrained optimization --- path loss model --- particle swarm optimization --- beacon --- absolute position system --- cooperative algorithm --- intercepting vehicles --- robot framework --- UWB sensors --- Internet of Things (IoT) --- wireless sensor network (WSN) --- switched-beam antenna --- electronically steerable parasitic array radiator (ESPAR) antenna --- received signal strength (RSS) --- fingerprinting --- down-conversion --- GPS --- navigation --- RF repeaters --- up-conversion

Choose an application

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

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot.

Technology: general issues --- Energy industries & utilities --- dynamic objects identification and localization --- laser cluster --- radial velocity similarity --- Pearson correlation coefficient --- particle filter --- trilateral indoor positioning --- RSSI filter --- RSSI classification --- stability --- accuracy --- inertial navigation system --- artificial neural network --- motion tracking --- sensor fusion --- indoor navigation system --- indoor positioning --- indoor navigation --- radiating cable --- leaky feeder --- augmented reality --- Bluetooth --- indoor positioning system --- smart hospital --- indoor --- positioning --- visually impaired --- deep learning --- multi-layered perceptron --- inertial sensor --- smartphone --- multi-variational message passing (M-VMP) --- factor graph (FG) --- second-order Taylor expansion --- cooperative localization --- joint estimation of position and clock --- RTLS --- indoor positioning system (IPS) --- position data --- industry 4.0 --- traceability --- product tracking --- fingerprinting localization --- Bluetooth low energy --- Wi-Fi --- performance metrics --- positioning algorithms --- location source optimization --- fuzzy comprehensive evaluation --- DCPCRLB --- UAV --- unmanned aerial vehicles --- NWPS --- indoor positioning systems --- GPS denied --- GNSS denied --- autonomous vehicles --- visible light positioning --- mobile robot --- calibration --- appearance-based localization --- computer vision --- Gaussian processes --- manifold learning --- robot vision systems --- image manifold --- descriptor manifold --- indoor fingerprinting localization --- Gaussian filter --- Kalman filter --- received signal strength indicator --- channel state information --- indoor localization --- visual-inertial SLAM --- constrained optimization --- path loss model --- particle swarm optimization --- beacon --- absolute position system --- cooperative algorithm --- intercepting vehicles --- robot framework --- UWB sensors --- Internet of Things (IoT) --- wireless sensor network (WSN) --- switched-beam antenna --- electronically steerable parasitic array radiator (ESPAR) antenna --- received signal strength (RSS) --- fingerprinting --- down-conversion --- GPS --- navigation --- RF repeaters --- up-conversion --- n/a