26,446 research outputs found
New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources
Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications
The supersymmetric affine Yangian
The affine Yangian of is known to be isomorphic to , the -algebra that characterizes the bosonic higher spin --
CFT duality. In this paper we propose defining relations of the Yangian that
are relevant for the superconformal version of . Our construction is based on the observation that the superconformal algebra contains two commuting
bosonic algebras, and that the additional generators
transform in bi-minimal representations with respect to these two algebras. The
corresponding affine Yangian can therefore be built up from two affine Yangians
of by adding in generators that transform appropriately.Comment: 35 pages, 5 figure
Criticality in Translation-Invariant Parafermion Chains
In this work we numerically study critical phases in translation-invariant
parafermion chains with both nearest- and next-nearest-neighbor
hopping terms. The model can be mapped to a spin model with
nearest-neighbor couplings via a generalized Jordan-Wigner transformation and
translation invariance ensures that the spin model is always self-dual. We
first study the low-energy spectrum of chains with only nearest-neighbor
coupling, which are mapped onto standard self-dual clock models.
For we match the numerical results to the known conformal field
theory(CFT) identification. We then analyze in detail the phase diagram of a
chain with both nearest and next-nearest neighbor hopping and six
critical phases with central charges being , 1 or 2 are found. We find
continuous phase transitions between and phases, while the phase
transition between and is conjectured to be of
Kosterlitz-Thouless type.Comment: published versio
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Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning
Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach
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Leveraging Local Intra-Core Information to Increase Global Performance in Block-Based Design of Systems-on-Chip
Latency-insensitive design is a methodology for system-on-chip (SoC) design that simplifies the reuse of intellectual property cores and the implementation of the communication among them. This simplification is based on a system-level protocol that decouples the intra-core logic design from the design of the inter-core communication channels. Each core is encapsulated within a shell, a synthesized logic block that dynamically controls its operation to interface it with the rest of the SoC and to absorb any latency variations on its I/O signals. In particular, a shell stalls a core whenever new valid data are not available on the input channels or a down-link core has requested a delay in the data production on the output channels. We study how knowledge about the internal logic structure of a core can be applied to the design of its shell to improve the overall system-level performance by avoiding unnecessary local stalling. We introduce the notion of functional independence conditions (FIC) and present a novel circuit design of a generic shell template that can leverage FIC. We propose a procedure for the logic synthesis of a FIC-shell instance that is only based on the analysis of the intra-core logic and does not require any input from the designers. Finally, we present a comprehensive experimental analysis that shows the performance benefits and limited design overhead of the proposed technique. This includes the semi-custom design of an SoC, an ultra-wideband baseband transmitter, using a 90nm industrial standard cell library
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