Low-energy kink in the nodal dispersion of copper-oxide superconductors: Insights from Dynamical Mean Field Theory

Motivated by the observation in copper-oxide high-temperature superconductors, we investigate the appearance of kinks in the electronic dispersion due to coupling to phonons for a system with strong electronic repulsion. We study a Hubbard model supplemented by an electron-phonon coupling of Holstein type within Dynamical Mean Field Theory (DMFT) utilizing Numerical Renormalization Group as impurity solver. Paramagnetic DMFT solutions in the presence of large repulsion show a kink only for large values of the electron-phonon coupling $\lambda$ or large doping and, contrary to the conventional electron-phonon theory, the position of such a kink can be shifted to energies larger than the renormalized phonon frequency $\omega_0^r$. When including antiferromagnetic correlations we find a stronger effect of the electron-phonon interaction on the electronic dispersion due to a cooperative effect and a visible kink at $\omega_0^r$, even for smaller $\lambda$. Our results provide a scenario of a kink position increasing with doping, which could be related to recent photoemission experiments on Bi-based cuprates.Comment: 10 pages, 10 figures; additional referene

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed

Data acquisition systems with intelligent trigger capability

Two data acquisition systems, based on two solutions for improving the performance, are here presented. The first one, fully analog, is able to generate a voltage impulse at the occurrence of a transient phenomenon on the stationary waveform being monitored. In the second system the acquisition process is regulated by absolute value of the derivative of the signal under analysis. This system is realized with Field Programmable Gate Array technology. All theoretical relations underlying the proposed solutions are first discussed. Their most relevant hardware and software features are then described. A suitable measurement apparatus is set up for assessing the performance of both solutions, and the obtained results are finally given. (c) 2005 Elsevier Ltd. All rights reserved

A Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks

We address the problem of estimating the state of a differentially flat system from measurements that may be corrupted by an adversarial attack. In cyber-physical systems, malicious attacks can directly compromise the system's sensors or manipulate the communication between sensors and controllers. We consider attacks that only corrupt a subset of sensor measurements. We show that the possibility of reconstructing the state under such attacks is characterized by a suitable generalization of the notion of s-sparse observability, previously introduced by some of the authors in the linear case. We also extend our previous work on the use of Satisfiability Modulo Theory solvers to estimate the state under sensor attacks to the context of differentially flat systems. The effectiveness of our approach is illustrated on the problem of controlling a quadrotor under sensor attacks.Comment: arXiv admin note: text overlap with arXiv:1412.432

Synthesis of On-Chip Interconnection Structures:From Point-to-Point Links to Networks-on-Chip

Packet-switched networks-on-chip (NOC) have been advocated as the solution to the challenge of organizing efficient and reliable communication structures among the components of a system-on-chip (SOC). A critical issue in designing a NOC is to determine its topology given the set of point-to-point communication requirements among these components. We present a novel approach to on-chip communication synthesis that is based on the iterative combination of two efficient computational steps: (1) an application of the k-Median algorithm to coarsely determine the global communication structure (which may turned out not be a network after all), and a (2) a variation of the shortest-path algorithm in order to finely tune the data flows on the communication channels. The application of our method to case studies taken from the literature shows that we can automatically synthesize optimal NOC topologies for multi-core on-chip processors and it offers new insights on why NOC are not necessarily a value proposition for some classes of applcation-specific SOCs

MulCh: a Multi-layer Channel Router using One, Two, and Three Layer Partitions

Chameleon, a channel router for three layers of interconnect, has been implemented to accept specification of an arbitrary number of layers. Chameleon is based on a strategy of decomposing the multilayer problem into two- and three-layer problems in which one of the layers is reserved primarily for vertical wire runs and the other layer(s) for horizontal runs. In some situations, however, it is advantageous to consider also layers that allow the routing of entire nets, using both horizontal and vertical wires. MulCh is a multilayer channel router that extends the algorithms of Chameleon in this direction. MulCh can route channels with any number of layers and automatically chooses a good assignment of wiring strategies to the different layers. In test cases, MulCh shows significant improvement over Chameleon in terms of channel width, net length, and number of vias

Hybrid systems in automotive electronics design

Automotive electronic design is certainly one of the most attractive and promising application domains for hybrid system techniques. Some successful hybrid system applications to automotive model development and control algorithm design have already been reported in the literature. However, despite the significant advances achieved in the past few years, hybrid methods are in general still not mature enough for their effective introduction in the automotive industry design processes at large. In this paper, we take a broad view of the development process for embedded control systems in the automotive industry with the purpose of identifying challenges and additional opportunities for hybrid systems. We identify critical steps in the design flow and extract a number of open problems where hybrid system technology might play an important role

Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning

Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the power-constrained CPP problem with recharge for battery-limited unmanned aerial vehicles (UAVs). In this problem, a notable challenge emerges from integrating recharge journeys into the overall coverage strategy, highlighting the intricate task of making strategic, long-term decisions. We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. We further provide the agent with a position history to handle emergent state loops caused by the recharge capability. Our approach outperforms a baseline heuristic, generalizes to different target zones and maps, with limited generalization to unseen maps. We offer valuable insights into DRL algorithm design for long-horizon problems and provide a publicly available software framework for the CPP problem.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Quasiparticle evolution and pseudogap formation in V2O3: An infrared spectroscopy study

The infrared conductivity of V2O3 is measured in the whole phase diagram. Quasiparticles appear above the Neel temperature TN and eventually disappear further enhancing the temperature, leading to a pseudogap in the optical spectrum above 425 K. Our calculations demonstrate that this loss of coherence can be explained only if the temperature dependence of lattice parameters is considered. V2O3 is therefore effectively driven from the metallic to the insulating side of the Mott transition as the temperature is increased.Comment: 5 pages, 3 figure