Today, many embedded or cyber-physical systems, e.g., in the automotive domain, comprise several control applications, sharing the same platform. It is well known that such resource sharing leads to complex temporal behaviors that degrades the quality of control, and more importantly, may even jeopardize stability in the worst case, if not properly taken into account. In this thesis, we consider embedded control or cyber-physical systems, where several control applications share the same processing unit. The focus is on the control-scheduling co-design problem, where the controller and scheduling parameters are jointly optimized. The fundamental difference between control applications and traditional embedded applications motivates the need for novel methodologies for the design and optimization of embedded control systems. This thesis is one more step towards correct design and optimization of embedded control systems. Offline and online methodologies for embedded control systems are covered in this thesis. The importance of considering both the expected control performance and stability is discussed and a control-scheduling co-design methodology is proposed to optimize control performance while guaranteeing stability. Orthogonal to this, bandwidth-efficient stabilizing control servers are proposed, which support compositionality, isolation, and resource-efficiency in design and co-design. Finally, we extend the scope of the proposed approach to non-periodic control schemes and address the challenges in sharing the platform with self-triggered controllers. In addition to offline methodologies, a novel online scheduling policy to stabilize control applications is proposed.

"System-Level Design Techniques for Energy-Efficient Embedded Systems will be of interest to advanced undergraduates, graduate students, and designers."--BOOK JACKET.

An introduction to the engineering principles of embedded systems, with a focus on modeling, design, and analysis of cyber-physical systems. The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes. The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems.

"IEEE Press is pleased to bring you this Second Edition of Phillip A. Laplante's best-selling and widely-acclaimed practical guide to building real-time systems. This book is essential for improved system designs, faster computation, better insights, and ultimate cost savings. Unlike any other book in the field, REAL-TIME SYSTEMS DESIGN AND ANALYSIS provides a holistic, systems-based approach that is devised to help engineers write problem-solving software. Laplante's no-nonsense guide to real-time system design features practical coverage of: Related technologies and their histories Time-saving tips * Hands-on instructions Pascal code Insights into decreasing ramp-up times and more!"

This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.

Development of new functionality and smart systems for different types of vehicles is accelerating with the advent of new emerging technologies such as connected and autonomous vehicles. To ensure that these new systems and functions work as intended, flexible and credible evaluation tools are necessary. One example of this type of tool is a driving simulator, which can be used for testing new and existing vehicle concepts and driver support systems. When a driver in a driving simulator operates it in the same way as they would in actual traffic, you get a realistic evaluation of what you want to investigate. Two advantages of a driving simulator are (1.) that you can repeat the same situation several times over a short period of time, and (2.) you can study driver reactions during dangerous situations that could result in serious injuries if they occurred in the real world. An important component of a driving simulator is the vehicle model, i.e., the model that describes how the vehicle reacts to its surroundings and driver inputs. To increase the simulator realism or the computational performance, it is possible to divide the vehicle model into subsystems that run on different computers that are connected in a network. A subsystem can also be replaced with hardware using so-called hardware-in-the-loop simulation, and can then be connected to the rest of the vehicle model using a specified interface. The technique of dividing a model into smaller subsystems running on separate nodes that communicate through a network is called distributed simulation. This thesis investigates if and how a distributed simulator design might facilitate the maintenance and new development required for a driving simulator to be able to keep up with the increasing pace of vehicle development. For this purpose, three different distributed simulator solutions have been designed, built, and analyzed with the aim of constructing distributed simulators, including external hardware, where the simulation achieves the same degree of realism as with a traditional driving simulator. One of these simulator solutions has been used to create a parameterized powertrain model that can be configured to represent any of a number of different vehicles. Furthermore, the driver's driving task is combined with the powertrain model to monitor deviations. After the powertrain model was created, subsystems from a simulator solution and the powertrain model have been transferred to a Modelica environment. The goal is to create a framework for requirement testing that guarantees sufficient realism, also for a distributed driving simulation. The results show that the distributed simulators we have developed work well overall with satisfactory performance. It is important to manage the vehicle model and how it is connected to a distributed system. In the distributed driveline simulator setup, the network delays were so small that they could be ignored, i.e., they did not affect the driving experience. However, if one gradually increases the delays, a driver in the distributed simulator will change his/her behavior. The impact of communication latency on a distributed simulator also depends on the simulator application, where different usages of the simulator, i.e., different simulator studies, will have different demands. We believe that many simulator studies could be performed using a distributed setup. One issue is how modifications to the system affect the vehicle model and the desired behavior. This leads to the need for methodology for managing model requirements. In order to detect model deviations in the simulator environment, a monitoring aid has been implemented to help notify test managers when a model behaves strangely or is driven outside of its validated region. Since the availability of distributed laboratory equipment can be limited, the possibility of using Modelica (which is an equation-based and object-oriented programming language) for simulating subsystems is also examined. Implementation of the model in Modelica has also been extended with requirements management, and in this work a framework is proposed for automatically evaluating the model in a tool.

Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.