Preface Thi s textbook is about signals and systems, a discipline rooted in the intellectual tradition of electrical engineering (EE). This tradition, however, has evolved in unexpected ways. EE has lost its tight coupling with the " electrical. " Electricity provides the impetus, the potential, but not the body of the subject. How else could microelectromechanical systems (MEMS) become so important in EE? Is this not mechanical engineering? Or signal processing? Is this not mathe-matics? Or digital networking? Is this not computer science? How is it that control system techniques are profitably applied to aeronautical systems, structural mechanics , electrical systems, and options pricing? This book approaches signals and systems from a computational point of view. It is intended for students interested in the modern, highly digital problems of electrical engineering, computer science, and computer engineering. In particular , the approach is applicable to problems in computer networking, wireless communication systems, embedded control, audio and video signal processing, and, of course, circuits. A more traditional introduction to signals and systems would be biased toward the latter application, circuits. It would focus almost exclusively on linear time-invariant systems, and would develop continuous-time models first, with discrete-time models then treated as an advanced topic. The discipline, after all, grew out of the context of circuit analysis. But it has changed. Even pure EE xiii xiv Preface graduates are more likely to write software than to push electrons, and yet we still recognize them as electrical engineers. The approach in this book benefits students by showing from the start that the methods of signals and systems are applicable to software systems, and most interestingly, to systems that mix computers with physical devices such as circuits, mechanical control systems, and physical media. Such systems have become pervasive, and profoundly affect our daily lives. The shift away from circuits implies some changes in the way the methodology of signals and systems is presented. While it is still true that a voltage that varies over time is a signal, so is a packet sequence on a network. This text defines signals to cover both. While it is still true that an RLC circuit is a system, so is a computer program for decoding Internet audio. This text defines systems to cover both. While for some systems the state is still captured adequately by variables in a differential equation, for many it is now the values in …
The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.
Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.