Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Addressing the Smart Systems Design Challenge: The SMAC Platform

This article presents the concepts, the organization, and the preliminary application results of SMAC, a smart systems co-design platform. The SMAC platform, which has been developed as Integrated Project (IP) of the 7th ICT Call under the Objective 3.2 \u201cSmart components and Smart Systems integration\u201d addresses the challenges of the integration of heterogeneous and conflicting domains that emerge in the design of smart systems. SMAC includes methodologies and EDA tools enabling multi-disciplinary and multi-scale modelling and design, simulation of multidomain systems, subsystems and components at different levels of abstraction, system integration and exploration for optimization of functional and non-functional metrics. The article presents the preliminary results obtained by adopting the SMAC platform for the design of a limb tracking smart system

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Collected software engineering papers, volume 9

This document is a collection of selected technical papers produced by participants in the Software Engineering Laboratory (SEL) from November 1990 through October 1991. The purpose of the document is to make available, in one reference, some results of SEL research that originally appeared in a number of different forums. This is the ninth such volume of technical papers produced by the SEL. Although these papers cover several topics related to software engineering, they do not encompass the entire scope of SEL activities and interests. For the convenience of this presentation, the eight papers contained here are grouped into three major categories: (1) software models studies; (2) software measurement studies; and (3) Ada technology studies. The first category presents studies on reuse models, including a software reuse model applied to maintenance and a model for an organization to support software reuse. The second category includes experimental research methods and software measurement techniques. The third category presents object-oriented approaches using Ada and object-oriented features proposed for Ada. The SEL is actively working to understand and improve the software development process at GSFC

The COMPLEX reference framework for HW/SW co-design and power management supporting platform-based design-space exploration

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Enabling Practical and Accessible Automatic Droplet Microfluidics Platforms

Droplet microfluidics has emerged as an innovative technology enabling high-sensitivity, high-resolution chemical and biological analyses via precise manipulation of picoliter-to-microliter fluid droplets. The ideal end goal of this technology is a general-purpose droplet microfluidics platform (DMP) composed of simple building blocks or modules that can be configured to perform arbitrary manipulations and analyses of individual droplets autonomously, returning desired outputs (e.g. nanoparticle synthesis) or insights (e.g. heavy metal detection) to the end-user. Although numerous innovations in droplet manipulation have emerged in the literature, most existing techniques --- broadly categorized as passive vs active --- optimize for a single application and act on continuous droplet trains, hence are difficult to generalize to arbitrary manipulation of individual droplets. Passive techniques rely on specific microfluidic chip geometries to be designed by a skilled user to perform a fixed sequence of droplet manipulations, and thus cannot be used for individual droplet control. Most active techniques embed custom actuators (electrodes, membranes, etc) within a passive system which only allows individual droplet control in localized areas, limiting the precision and resolution of droplet manipulations. A simpler and more generic technique is pressure-driven feedback control, in which droplets are sensed visually within simple passive chip geometries (e.g. T-junctions) and actuated by off-chip pumps that adjust chip inlet pressures in response to visual feedback. This approach shows that individual droplets can be stabilized and driven to arbitrary locations on-chip without the need for complex chip designs or embedded actuators, opening the door to modular automation. However, bridging the gap from this proof-of-concept to a fully automated modular platform for non-expert users requires overcoming significant practicality and accessibility challenges. Existing feedback control systems for droplet manipulation ignore time-varying behavior in the system, which gradually degrades performance and reliability, necessitating frequent manual tuning and calibration. Additionally, current software workflows require the end-user to manually set up each droplet manipulation, which does not generalize to longer manipulation sequences necessary for practical applications. Moreover, standard pressure-driven flow generation methods are either too slow and imprecise for individual droplet control, or too complex and costly to be effectively modularized. This thesis aims to address these key challenges in feedback control, software workflow, and droplet actuation to pave the way for modular automated DMPs that are practical and accessible for end-users. On feedback control, a new adaptive control system is designed to automatically perform model parameter identification online, compensating for changes in system dynamics as droplet manipulations are performed. Regarding software, a new DMP workflow is developed to allow end users to validate and execute arbitrary manipulation sequences automatically. For pressure-driven flow generation, off-the-shelf piezoelectric micropumps are evaluated as a modular, low-cost alternative to existing methods, demonstrating comparable performance in droplet manipulation

Design Tools for Dynamic, Data-Driven, Stream Mining Systems

The proliferation of sensing devices and cost- and energy-efficient embedded processors has contributed to an increasing interest in adaptive stream mining (ASM) systems. In this class of signal processing systems, knowledge is extracted from data streams in real-time as the data arrives, rather than in a store-now, process later fashion. The evolution of machine learning methods in many application areas has contributed to demands for efficient and accurate information extraction from streams of data arriving at distributed, mobile, and heterogeneous processing nodes. To enhance accuracy, and meet the stringent constraints in which they must be deployed, it is important for ASM systems to be effective in adapting knowledge extraction approaches and processing configurations based on data characteristics and operational conditions. In this thesis, we address these challenges in design and implementation of ASM systems. We develop systematic methods and supporting design tools for ASM systems that integrate (1) foundations of dataflow modeling for high level signal processing system design, and (2) the paradigm on Dynamic Data-Driven Application Systems (DDDAS). More specifically, the contributions of this thesis can be broadly categorized in to three major directions: 1. We develop a new design framework that systematically applies dataflow methodologies for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems environment (LiD4E). LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of stream mining topologies. Using this novel application of dataflow methods, LiD4E facilitates the efficient and reliable mapping and adaptation of classifier topologies into implementations on embedded platforms. 2. We introduce new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource- and energy-constrained embedded environments, such as unmanned areal vehicles (UAVs), mobile communication platforms, and wireless sensor networks. We develop a design and implementation framework for multi-mode, data driven embedded signal processing systems, where application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application. 3. We introduce new methods for multiobjective, system-level optimization that have been incorporated into the LiD4E design tool described previously. More specifically, we develop new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (e.g., precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints