In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and post-processing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure prediction is a crucial technique for self-managing resource burdens. Failure events in coalition systems exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. We further utilize the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPREFECTs), which explores correlations among failures and forecasts the time-between-failure of future instances. We evaluate the performance of hPREFECTs in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. Experimental results show the system achieves more than 76% accuracy in offline prediction and more than 70% accuracy in online prediction during the time from May 2006 to April 2007.

Data-driven turbulence modeling has been considered an effective method for improving the prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed to solve the discrepancy of traditional turbulence modeling by acquiring specific patterns from high-fidelity data through machine learning methods, such as artificial neural networks. The present study focuses on the unsmoothness and prediction error problems from the aspect of feature selection and processing. The selection criteria for the input features are summarized, and an effective input set is constructed. The effect of the computation grid on the smoothness is studied. A modified feature decomposition method for the spatial orientation feature of the Reynolds stress is proposed. The improved machine learning framework is then applied to the periodic hill database with notably varying geometries. The results of the modified method show significant enhancement in the prediction accuracy and smoothness, including the shape and size of separation areas and the friction and pressure distributions on the wall, which confirms the validity of the approach.

Görtler vortices developed in laminar boundary layer experience remarkable changes when the flow is subjected to compressibility effects. In the present study, five $\mathit{Ma}$ numbers, covering incompressible to hypersonic flows, at $\mathit{Ma}=0.015$ , 1.5, 3.0, 4.5 and 6.0 are specified to illustrate these effects. Görtler vortices in subsonic and moderate supersonic flows ( $\mathit{Ma}=0.015$ , 1.5 and 3.0) are governed by the conventional wall-layer mode (mode W). In hypersonic flows ( $\mathit{Ma}=4.5$ , 6.0), the trapped-layer mode (mode T) becomes dominant. This difference is maintained and intensifies downstream leading to different scenarios of secondary instabilities. The linear and nonlinear development of Görtler vortices which are governed by dominant modal disturbances are investigated with direct marching of the nonlinear parabolic equations. The secondary instabilities of Görtler vortices set in when the resulting streaks are adequately developed. They are studied with Floquet theory at multiple streamwise locations. The secondary perturbations become unstable downstream following the sequence of sinuous mode type I, varicose mode and sinuous mode type II, indicating an increasing threshold amplitude. Onset conditions are determined for these modes. The above three modes can each have the largest growth rate under the right conditions. In the hypersonic cases, the threshold amplitude $A(u)$ is dramatically reduced, showing the significant impact of the thermal streaks. To investigate the parametric effect of the spanwise wavenumber, three global wavenumbers ( $B=0.5$ , 1.0 and $2.0\times 10^{-3}$ ) are specified. The relationship between the dominant mode (sinuous or varicose) and the spanwise wavenumber of Görtler vortices found in incompressible flows (Li & Malik, J. Fluid Mech., vol. 297, 1995, pp. 77–100) is shown to be not fully applicable in high-speed cases. The sinuous mode becomes the most dangerous, regardless of the spanwise wavelength when $\mathit{Ma}>3.0$ . The subharmonic type can be the most dangerous mode while the detuned type can be neglected, although some of the sub-dominant secondary modes reach their peak growth rates under detuned states.

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructures. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software faults and environmental factors. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect anomalous cloud behaviors, we need to monitor the cloud execution and collect runtime cloud performance data. These data consist of values of performance metrics for different types of failures, which display different correlations with the performance metrics. In this paper, we present an adaptive anomaly identification mechanism that explores the most relevant principal components of different failure types in cloud computing infrastructures. It integrates the cloud performance metric analysis with filtering techniques to achieve automated, efficient, and accurate anomaly identification. The proposed mechanism adapts itself by recursively learning from the newly verified detection results to refine future detections. We have implemented a prototype of the anomaly identification system and conducted experiments in an on-campus cloud computing environment and by using the Google data center traces. Our experimental results show that our mechanism can achieve more efficient and accurate anomaly detection than other existing schemes.