Can Wigner distribution functions with collisions satisfy complete positivity and energy conservation?

To avoid the computational burden of many-body quantum simulation, the interaction of an electron with a photon (phonon) is typically accounted for by disregarding the explicit simulation of the photon (phonon) degree of freedom and just modeling its effect on the electron dynamics. For quantum models developed from the (reduced) density matrix or its Wigner–Weyl transformation, the modeling of collisions may violate complete positivity (precluding the typical probabilistic interpretation). In this paper, we show that such quantum transport models can also strongly violate the energy conservation in the electron–photon (electron–phonon) interactions. After comparing collisions models to exact results for an electron interacting with a photon, we conclude that there is no fundamental restriction that prevents a collision model developed within the (reduced) density matrix or Wigner formalisms to satisfy simultaneously complete positivity and energy conservation. However, at the practical level, the development of such satisfactory collision model seems very complicated. Collision models with an explicit knowledge of the microscopic state ascribed to each electron seems recommendable (Bohmian conditional wavefunction), since they allow to model collisions of each electron individually in a controlled way satisfying both complete positivity and energy conservation.

     

Elman neural networks for characterizing voids in welded strips: a study

Within the framework of aging materials inspection, one of the most important aspects regarding defects detection in metal welded strips. In this context, it is important to plan a method able to distinguish the presence or absence of defects within welds as well as a robust procedure able to characterize the defect itself. In this paper, an innovative solution that exploits a rotating magnetic field is presented. This approach has been carried out by a finite element model. Within this framework, it is necessary to consider techniques able to offer advantages in terms of sensibility of analysis, strong reliability, speed of carrying out, low costs: its implementation can be a useful support for inspectors. To this aim, it is necessary to solve inverse problems which are mostly ill-posed; in this case, the main problems consist on both the accurate formulation of the direct problem and the correct regularization of the inverse electromagnetic problem. We propose a heuristic inversion, regularizing the problem by the use of an Elman network. Experimental results are obtained using a database created through numerical modeling, confirming the effectiveness of the proposed methodology.     

Vision‐based control architecture for human–robot hand‐over applications

Human–robot collaboration will be an essential part of the production processes in the factories of tomorrow. In this paper, a human–robot hand‐over control strategy is presented. Human and robot can be both giver and receiver. A path‐planning algorithm drives the robotic manipulator towards the hand of the human and permits to adapt the pose of the tool center point of the robot to the pose of the hand of the human worker. The movements of the operator are acquired with a multi 3D‐sensors setup so to avoid any possible occlusion related to the presence of the robot or other dynamic obstacles. Estimation of the predicted position of the hand is performed to reduce the waiting time of the operator during the hand‐over task. The hardware setup is described, and the results of experimental tests, conducted to verify the effectiveness of the control strategy, are presented and discussed.     

A CUDA-powered method for the feature extraction and unsupervised analysis of medical images

The Journal of Supercomputing - Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are...     

CMOS Integrated Lock-in Readout Circuit for FET Terahertz Detectors

In this paper, a switched-capacitor readout circuit topology integrated with a THz antenna and field-effect transistor detector is analyzed, designed, and fabricated in a 0.13-μm standard CMOS technology. The main objective is to perform amplification and filtering of the signal, as well as subtraction of background in case of modulated source, in order to avoid the need for an external lock-in amplifier, in a compact implementation. A maximum responsivity of 139.7 kV/W, and a corresponding minimum NEP of 2.2 nW/√Hz, was obtained with a two-stage readout circuit at 1 kHz modulation frequency. The presented switched-capacitor circuit is suitable for implementation in pixel arrays due to its compact size and power consumption (0.014 mm² and 36 μW).     

Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.     

Identification and Rejuvenation of NBTI-Critical Logic Paths in Nanoscale Circuits

The Negative Bias Temperature Instability (NBTI) phenomenon is agreed to be one of the main reliability concerns in nanoscale circuits. It increases the threshold voltage of pMOS transistors, thus, slows down signal propagation along logic paths between flip-flops. NBTI may cause intermittent faults and, ultimately, the circuit’s permanent functional failures. In this paper, we propose an innovative NBTI mitigation approach by rejuvenating the nanoscale logic along NBTI-critical paths. The method is based on hierarchical identification of NBTI-critical paths and the generation of rejuvenation stimuli using an Evolutionary Algorithm. A new, fast, yet accurate model for computation of NBTI-induced delays at gate-level is developed. This model is based on intensive SPICE simulations of individual gates. The generated rejuvenation stimuli are used to drive those pMOS transistors to the recovery phase, which are the most critical for the NBTI-induced path delay. It is intended to apply the rejuvenation procedure to the circuit, as an execution overhead, periodically. Experimental results performed on a set of designs demonstrate reduction of NBTI-induced delays by up to two times with an execution overhead of 0.1 % or less. The proposed approach is aimed at extending the reliable lifetime of nanoelectronics.     

SERVICES,SEMANTICS, AND STANDARDS: ELEMENTS OF A LEARNING GRID INFRASTRUCTURE

There has been considerable political pressure and much hope invested in the use of communication and information technologies to provide wider access to education, while improving quality and reducing costs. Unfortunately, many of the responses to the challenge of these aspirations have consisted of simple Web technology-driven products, which have failed to progress effective learning. In this paper we outline the characteristics and pedagogical goals of a learning paradigm that is used to drive the technical requirements, rather than being constrained by what is easily achieved in XHTML. We identify and explain the key roles played by services, semantics, and standards in meeting pedagogical goals of novel learning situations, and illustrate with some scenarios that build bridges between traditional learning contexts and future possibilities. Crucially, we explain why we have adopted Grid technologies in the European Learning Grid Infrastructure (ELeGI) research program.     

LEARNING AND COORDINATION FOR AUTONOMOUS INTERSECTION CONTROL

Future urban road traffic management is an example of a socially relevant problem that can be modeled as a large-scale, open, distributed system, composed of many autonomous interacting agents, which need to be controlled in a decentralized manner. In this context, advanced, reservation-based, intersection control—where autonomous vehicles controlled entirely by agents interact with a coordination facility that controls an intersection, to avoid collisions and minimize delays—will be a possible scenario in the near future. In this article, we seize the opportunities for multiagent learning offered by such a scenario, studying i) how vehicles, when approaching a reservation-based intersection, can coordinate their actions in order to improve their crossing times, and therefore, speed up the traffic flow through the intersection, and ii) how a set of reservation-based intersections can cooperatively act over an entire network of intersections in order to minimize travel times.