A profile-driven statistical analysis framework for the design optimization of soft real-time applications

Multimedia applications became an integral part of the ubiquitous computing environment in general-purpose systems. However, current general-purpose operating systems cannot effectively support their soft real-time requirements. This paper presents a reservation-based preemptive earliest deadline first (R-EDF) algorithm to provide a predictable scheduling framework in an open shared environment. The major contributions of R-EDF are: it delivers timing guarantees to four soft real-time task classes such as period class with constant and various processing time, aperiodic class and event-based class; it provides starvation protection for best-effort tasks; it provides overrun protection and handling so that an overrun task will not cause other tasks to miss deadlines and will finish its work within an acceptable and predictable time bound; and it allows optimistic reservation and incurs low overhead cost for reservation and admission control. Our experimental results show the low overhead and correctness of R-EDF even when there is resource contention.

CORBA is an important standard middleware used in the development of distributed applications. It has also been used with distributed real-time applications, through its extension for real-time systems, RT-CORBA. RT-CORBA includes many mechanisms to reduce the nondeterminism associated with ordinary CORBA. These mechanisms can be used to provide guarantees for hard real-time systems if the right support from the operating system and network protocols is available. RT-CORBA mechanisms can also be used to improve the timing behavior of soft real-time applications, when the lower layers are not able to provide guarantees. This paper proposes an adaptive scheduling service in the context of RT-CORBA to support the implementation of distributed soft real-time applications. The proposal is based on the adaptation of task periods, so as to reduce system load while still trying to meet the original deadline of all tasks. This is a best-effort approach that dynamically provides graceful degradation in case of overload. The adaptive service proposed in this paper is validated by a set of experiences based on mechanisms of RT-CORBA and TAO, the ORB implementation used.

Real-time systems are those systems in which the correctness of the system depends not only on the logical results of computation but also on the time at which the results are produced [Stankovic 1988]. They span a broad spectrum of complexity from very simple microcontrollers in embedded systems (a microprocessor controlling an automobile engine) to highly sophisticated, complex, and distributed systems (air traffic control for the continental United States). Other real-time systems include command and control systems, process control systems, and multimedia and high-speed communication systems. Some real-time systems are beginning to add expert systems and other AI technology, creating additional requirements and complexities. At least three major trends in the real-time and embedded systems field have had a major impact on its technology: the increased growth and sophistication of embedded systems, the development of more scientific and technological results for hard real-time systems, and the advent of distributed multimedia. Most embedded systems consist of a small microcontroller and limited software situated within some product such as a microwave oven or automobile. However, with the increasing sophistication of such systems, powerful microcontrollers and digital signal processing (DSP) chips are commonly used, as are off-the-shelf real-time operating systems and design and debugging tools. Many people working with embedded systems deal on a daily basis with sensors and data acquisition technology and systems; others construct architectures based on single-board computers (many are still 68000-based, but RISC processors are being used more and more) and buses such as the VME bus. Many people are involved with the programming and debugging of embedded systems, largely using the C programming language and cross development and debugging platforms. Embedded systems may or may not have real-time constraints. Hard real-time systems are those in which missing an important deadline can cause severe consequences, even death. In this area, many fundamental results have been developed. For example, in real-time scheduling, rate monotonic analysis has enabled careful evaluation of many practical systems; the concept and analysis of competitive algorithms has provided important scheduling bounds and limits; and on-line planning has added flexible and dynamic capabilities to real-time systems. Operating systems research has produced predictable primitives, time-constrained synchronization techniques, and reservation and admission-control paradigms. Many other results exist in real-time architecture, fault tolerance, communication protocols, specification and design tools, formal verification, databases and object-oriented systems. Emphasis on all these areas is expected to increase in the foreseeable future. Many hard real-time systems are embedded systems. Distributed multimedia have produced a new set of soft real-time requirements. Real-time principles lie at the heart of distributed multimedia, but without the concomitant high reliability

A profile-driven statistical analysis framework for the design optimization of soft real-time applications

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

Energy efficiency, reliability, and real-time are three key requirements of mission-critical embedded systems. Existing approaches over emphasize the worst case design of real-time embedded systems, which will lead to serious waste of resources. In this paper, we aim at the energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution durations regarding certain distributions. Thereby, we provide real-time guarantee with probability consideration. We utilize dynamic voltage and frequency scaling (DVFS) for saving energy, and also take into account the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. The design optimization problem is a typical multidimensional multiple-choice knapsack problem, which is NP-hard. We first propose a dynamic programming-based optimal algorithm to solve the problem. To reduce the time complexity, we then develop a ( $1+{\beta }$ )-approximation algorithm based on a binary search approach, where ${\beta }$ is the approximating factor. The approximation algorithm can obtain the near-optimal solution with at most ( $1{+\beta }$ ) times of optimal energy cost under given real-time and reliability constraints and has fully polynomial time complexity. Extensive experiments and a real-life synthetic application are conducted to evaluate the performance of the proposed techniques. Compared with existing approaches, the approximation approach can save much energy with low time overhead while guaranteeing the statistical deadline and reliability constraints.

The main objective of this paper is to present first and second-order sensitivity analysis of vibro-acoustic systems in the statistical energy analysis (SEA) frame work. Equations for computing these sensitivities for a general SEA model are obtained from two different approaches: (1) direct method and (2) adjoint method. The above equations are applied to a simple model of three plates, joined in the form of a ‘Z’, to minimize the total energy of one of the plates. It has been verified that these approaches lead to the same results and the difference between them is only with respect to the computational efficiency. The design sensitivity results calculated from the proposed analytical methods are compared with those obtained from the finite difference method, which show good agreement. The results of this paper can be useful to optimization of vibro-acoustic systems at the drawing board stage in the SEA framework.

In this paper we propose a new Tabu Search-based design optimization strategy for mixed-criticality systems implementing hard and soft real-time applications on the same platform. Our proposed strategy determined an implementation such that all hard real-time applications are schedulable and the quality of service of the soft real-time tasks is maximized. We have evaluated our strategy using an aerospace case study.

The interactions between tumor intrinsic processes and immune checkpoints can mediate immune evasion by cancer cells and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies and statistically validates tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The outcome is a catalog of tumor-immune checkpoint interaction maps for diverse immune-associated phenotypes. Applications of ImogiMap recapitulate the interaction of SERPINB9 and immune checkpoints with interferon gamma (IFNγ) expression. Our analyses suggest that CD86-CD70 and CD274-CD70 immunoregulatory interactions are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer, respectively. The open-source ImogiMap software and user-friendly web application will enable future applications of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and immunotherapy targets.

The critical issue of mutual interference in automotive radar leads to a growing number of processing algorithms for mitigation. Analyses of their performance have been mostly based on either simulations or on a small number of exemplary measurements. In this contribution, a corpus of real-world radar measurements is used, to which strongly varying simulated interferences are added. A statistical analysis framework is then applied to a set of state-of-the-art mitigation algorithms, studying the impact of highly complex object environments on their performance.

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

Conventional analysis of neuroscience data involves computing average neural activity over a group of trials and/or a period of time. This approach may be particularly problematic when assessing the response patterns of neurons to more than one simultaneously presented stimulus. in such cases the brain must represent each individual component of the stimuli bundle, but trial-and-time-pooled averaging methods are fundamentally unequipped to address the means by which multiitem representation occurs. We introduce and investigate a novel statistical analysis framework that relates the firing pattern of a single cell, exposed to a stimuli bundle, to the ensemble of its firing patterns under each constituent stimulus. Existing statistical tools focus on what may be called "first order stochasticity" in trial-to-trial variation in the form of unstructured noise around a fixed firing rate curve associated with a given stimulus. our analysis is based upon the theoretical premise that exposure to a stimuli bundle induces additional stochasticity in the cell's response pattern in the form of a stochastically varying recombination of its single stimulus firing rate curves. We discuss challenges to statistical estimation of such "second order stochasticity" and address them with a novel dynamic admixture point process (DAPP) model. DAPP is a hierarchical point process model that decomposes second order stochasticity into a Gaussian stochastic process and a random vector of interpretable features and facilitates borrowing of information on the latter across repeated trials through latent clustering. We illustrate the utility and accuracy of the DAPP analysis with synthetic data simulation studies. We present real-world evidence of second order stochastic variation with an analysis of monkey inferior colliculus recordings under auditory stimuli.

Aging and many neurological diseases cause progressive changes in brain morphology. Both subject-specific detection and measurement of these changes, as well as their population-based analysis are of great interest in many clinical studies. Generally, both problems are handled separately. However, as population-based knowledge facilitates subject-specific analysis and vice versa, we propose a unified statistical framework for subject-specific and population-based analysis of longitudinal brain MR image sequences of subjects suffering from the same neurological disease. The proposed method uses a maximum a posteriori formulation and the expectation maximization algorithm to simultaneously and iteratively segment all images in separate tissue classes, construct a global probabilistic 3D brain atlas and non-rigidly deform the atlas to each of the images to guide their segmentation. In order to enable a population-based analysis of the disease progression, an intermediate 4D probabilistic brain atlas is introduced, representing a discrete set of disease progression stages. The 4D atlas is simultaneously constructed with the 3D brain atlas by incorporating assignments of each input image (voxelwise) to a particular disease progression stage in the statistical framework. Moreover, these assignments enable both temporal and spatial subject-specific disease progression analysis. This includes detecting delayed or advanced disease progression and indicating the affected regions. The method is validated on a publicly available data set on which it shows promising results. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it's critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model. In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of "approximating the underlying function" rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments.

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.