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Author: Daniel Benjamin Rohweder Publisher: Springer Nature ISBN: 3658371145 Category : Technology & Engineering Languages : en Pages : 120
Book Description
Nowadays, most digital modulation schemes are based on conventional signal constellations that have no algebraic group, ring, or field properties, e.g. square quadrature-amplitude modulation constellations. Signal constellations with algebraic structure can enhance the system performance. For instance, multidimensional signal constellations based on dense lattices can achieve performance gains due to the dense packing. The algebraic structure enables low-complexity decoding and detection schemes. In this work, signal constellations with algebraic properties and their application in spatial modulation transmission schemes are investigated. Several design approaches of two- and four-dimensional signal constellations based on Gaussian, Eisenstein, and Hurwitz integers are shown. Detection algorithms with reduced complexity are proposed. It is shown, that the proposed Eisenstein and Hurwitz constellations combined with the proposed suboptimal detection can outperform conventional two-dimensional constellations with ML detection.
Author: Abdelkader Hameurlain Publisher: Springer Nature ISBN: 3662668637 Category : Computers Languages : en Pages : 175
Book Description
The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g. computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 53rd issue of Transactions on Large-scale Data and Knowledge-centered Systems, contains six fully revised selected regular papers. Topics covered include time series management from edge to cloud, segmentation for time series representation, similarity research, semantic similarity in a taxonomy, linked data semantic distance, linguistics-informed natural language processing, graph neural network, protected features, imbalanced data, causal consistency in distributed databases, actor model, and elastic horizontal scalability.
Author: Maura B. Paterson Publisher: Springer Nature ISBN: 3030926419 Category : Computers Languages : en Pages : 323
Book Description
This book constitutes the refereed proceedings of the 18th IMA International Conference on Cryptography and Coding, IMACC 2021, held in December 2021. Due to COVID 19 pandemic the conference was held virtually. The 14 papers presented were carefully reviewed and selected from 30 submissions. The conference focuses on a diverse set of topics both in cryptography and coding theory.
Author: Esa Hyytiä Publisher: Springer Nature ISBN: 3031312341 Category : Computers Languages : en Pages : 310
Book Description
This book constitutes the refereed conference proceedings of the 15th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2022, held in November 2022. Due to the safety concerns and travel restrictions caused by COVID-19, VALUETOOLS 2022 took place online in a live stream. The conference provides a world-leading and multidisciplinary venue for researchers and practitioners in diverse disciplines such as computer science, networks and telecommunications, operations research, optimization, control theory and manufacturing. The 18 full papers were carefully reviewed and selected from 47 submissions and are grouped in thematically as following: game theory; queueing models; applications; retrial queues; performance analysis and networking; distributed computing.
Author: Jana Fragemann Publisher: Springer Nature ISBN: 303125046X Category : Computers Languages : en Pages : 128
Book Description
This book constitutes the post-conference proceedings of the First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, held in conjunction with MICCAI 2022, in Singapore, on September22, 2022. The 8 full papers presented in this book together with one short paper were carefully reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wide range of medical applications, like brain age prediction, skull reconstruction and unsupervised pathology disentanglement.
Author: Yao Sun Publisher: John Wiley & Sons ISBN: 1119913918 Category : Technology & Engineering Languages : en Pages : 324
Book Description
Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.
Author: Patrick Murer Publisher: BoD – Books on Demand ISBN: 3866287585 Category : Computers Languages : en Pages : 230
Book Description
This thesis studies the capability of spiking recurrent neural network models to memorize dynamical pulse patterns (or firing signals). In the first part, discrete-time firing signals (or firing sequences) are considered. A recurrent network model, consisting of neurons with bounded disturbance, is introduced to analyze (simple) local learning. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity with an asymptotically nonvanishing number of bits per connection/synapse. These mathematical findings may be helpful for understanding the functionality of short-term memory and long-term memory in neuroscience. In the second part, firing signals in continuous-time are studied. It is shown how firing signals, containing firings only on a regular time grid, can be (robustly) memorized with a recurrent network model. In principle, the corresponding weights are obtained by supervised (quasi-Hebbian) multi-pass learning. The asymptotic memorization capacity is a nonvanishing number measured in bits per connection/synapse as its discrete-time analogon. Furthermore, the timing robustness of the memorized firing signals is investigated for different disturbance models. The regime of disturbances, where the relative occurrence-time of the firings is preserved over a long time span, is elaborated for the various disturbance models. The proposed models have the potential for energy efficient self-timed neuromorphic hardware implementations.
Author: Nikolaos Pappas
Author: Nikolaos Pappas
Author: Daniel Benjamin Rohweder
Author: Abdelkader Hameurlain
Author: Maura B. Paterson
Author: Mingzhe Chen
Author: Esa Hyytiä
Author: Jana Fragemann
Author: Zahir Tari
Author: Yao Sun
Author: Patrick Murer