Neuropsychological evidence for three distinct motion mechanisms

Published in final edited form as: Neurosci Lett. 2011 May 16; 495(2): 102–106. doi:10.1016/j.neulet.2011.03.048.We describe psychophysical performance of two stroke patients with lesions in distinct cortical regions in the left hemisphere. Both patients were selectively impaired on direction discrimination in several local and global second-order but not first-order motion tasks. However, only patient FD was impaired on a specific bi-stable motion task where the direction of motion is biased by object similarity. We suggest that this bi-stable motion task may be mediated by a high-level attention or position based mechanism indicating a separate neurological substrate for a high-level attention or position-based mechanism. Therefore, these results provide evidence for the existence of at least three motion mechanisms in the human visual system: a low-level first- and second-order motion mechanism and a high-level attention or position-based mechanism.Accepted manuscrip

Majorana neutrino decay in an Effective Approach

The search strategy or the finding of new effects for heavy neutrinos often relies on their different decay channels to detectable particles. In particular in this work we study the decay of a Majorana neutrino with interactions obtained from an effective general theory modeling new physics at the scale $\Lambda$. The results obtained are general because they are based in an effective theory and not in specific models. We are interested in relatively light heavy Majorana neutrinos, with masses lower than the $W$ mass ($m_N<m_W$). This mass range simplifies the study by reducing the possible decay modes. Moreover, we found that for $\Lambda\sim 1$ T$e$V, the neutrino plus photon channel could give explanation to different observations: we analyze the potentiality of the studied interactions to explain some neutrino-related problems like the MiniBooNE and SHALON anomalies. We show in different figures the dominant branching ratios and the decay length of the Majorana neutrino in this approach. This kind of heavy neutral leptons could be searched for in the LHC with the use of displaced vertices techniques. \Comment: 15 page, 5 figure

Source rock prospectivity of lower Carboniferous lacustrine strata, Anguille Group, Conche, Newfoundland

In Eastern Canada, in a regional Carboniferous rift system (Maritimes Basin Tectonostratigraphic Zone), there are lacustrine basins containing petroliferous source rocks. Near the northern limits for this complex, strata of the Cape Rouge Formation at Conche, Newfoundland, are thought to represent a nearby onshore facies analog for rocks laying farther offshore in the St. Anthony Basin. Four facies assemblages of mixed sandstone, siltstone, dolostone and black mudstone represent distinct stages of an underfilled lake-basin developed within a half-graben depocenter. The finest-grained facies assemblage, with TOC between 0.23-6.54 wt.%, has organic matter dominated by Type 1 kerogen. Maturation analysis places strata within the oil-generation window (Rₒ=0.5-1.01%); however, Rock-Eval results indicate little remaining potential for generating hydrocarbons. Based on findings from Conche, the St. Anthony Basin may contain similar half-graben lake-basins with Carboniferous source rock potential. However, source quality and timing for oil generation remain key risks for hydrocarbon exploration offshore

A topological approach to neural complexity

Considerable efforts in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X)introduced by Tononi et al. This work addresses the dependence of this measure on the topological features of a network in the case of gaussian stationary process. Both anlytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover the analytical result offers a straightforward algorithm to compute the complexity than the standard one.Comment: 6 pages, 4 figure

Development of new high-performance induction heating systems using model predictive control

Modern induction heating systems involve usually many non-linear systems with different constraints, making difficult implementing classical controllers. In this context, model predictive control arises as a powerful technique able to provide high performance controllers with advanced functionalities. This paper will discuss the benefits of this control technique for induction heating systems and will provide an example of application for domestic systems. As a conclusion, MPC will help powering future induction heating systems with improved control capabilities and performance

Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-Time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances

Multi-Resonant Power Converter for Improved Dual-Frequency Induction Heating

Industrial induction heating is a key manufacturing process due to its benefits in terms of efficiency, cleanliness, and high performance. These has made possible the extension of this technology to a wide range of industries from automotive to aeronautic, domestic or renewable energies. One of the main challenges still present is the design of a high performance and cost-effective process for those induction targets with complex geometries exposed to the magnetic field, being the most representative example the gears present in most mechanical systems. Usually, in order to optimize the heating process, multi-frequency induction heating systems are used, being often expensive and/or difficult to tune and control. The aim of this paper is, consequently, to propose a family of power converters able to supply the inductor system with two simultaneous frequencies in order to improve the heating process of the induction target. The proposed converter takes advantage of a multi-resonant network allowing full control of the output power delivered at each frequency and, at the same time, a compact and cost-effective implementation. The proposed converter is analyzed, designed and implemented, and experimental verification of its operation is provided in this paper