Reducing Interconnect Cost in NoC through Serialized Asynchronous Links

This work investigates the application of serialization as a means of reducing the number of wires in NoC combined with asynchronous links in order to simplify the clocking of the link. Throughput is reduced but savings in routing area and reduction in power could make this attractiv

Dual-mode hyperbolicity, supercanalization, and leakage in self-complementary metasurfaces

Anisotropic Self-Complementary Metasurfaces (SC-MTSs) are structures constituted by an alternation of complementary inductive and capacitive strips, which are "self-dual" according to Babinet's duality principle. They support the propagation of two orthogonally polarized surface-wave modes with the same phase velocity along the principal directions (i.e., along the strips and normal to them). The isofrequency dispersion curves of these modes are hyperbolas, and therefore, these MTSs fall in the category of hyperbolic MTSs. It is shown here that the hyperbolic dispersion curves may degenerate in same cases into almost straight lines, which implies that the velocity of energy transport is constantly directed along the same direction for any possible phasing orthogonal to the strips. In this circumstance, the SC-MTS can be conveniently used to design dual-polarized leaky-wave antennas by modulating the impedances of the complementary strips

Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals

To interact naturally and achieve mutual sympathy between humans and machines, emotion recognition is one of the most important function to realize advanced human-computer interaction devices. Due to the high correlation between emotion and involuntary physiological changes, physiological signals are a prime candidate for emotion analysis. However, due to the need of a huge amount of training data for a high-quality machine learning model, computational complexity becomes a major bottleneck. To overcome this issue, brain-inspired hyperdimensional (HD) computing, an energy-efficient and fast learning computational paradigm, has a high potential to achieve a balance between accuracy and the amount of necessary training data. We propose an HD Computing-based Multimodality Emotion Recognition (HDC-MER). HDCMER maps real-valued features to binary HD vectors using a random nonlinear function, and further encodes them over time, and fuses across different modalities including GSR, ECG, and EEG. The experimental results show that, compared to the best method using the full training data, HDC-MER achieves higher classification accuracy for both valence (83.2% vs. 80.1%) and arousal (70.1% vs. 68.4%) using only 1/4 training data. HDC-MER also achieves at least 5% higher averaged accuracy compared to all the other methods in any point along the learning curve

On the geometry of string duals with backreacting flavors

Making use of generalized calibrated geometry and G-structures we put the problem of finding string-duals with smeared backreacting flavor branes in a more mathematical setting. This more formal treatment of the problem allows us to easily smear branes without good coordinate representations, establish constraints on the smearing form and identify a topological central charge in the SUSY algebra. After exhibiting our methods for a series of well known examples, we apply them to the problem of flavoring a supergravity-dual to a d=2+1 dimensional N=2 super Yang-Mills-like theory. We find new solutions to both the flavored and unflavored systems. Interpretating these turns out to be difficult.Comment: 38 pages - Typos corrected and references added - As published in JHE

Dynamics of transcendental hÉnon maps III: Infinite entropy

Very little is currently known about the dynamics of non-polynomial entire maps in several complex variables. The family of transcendental Hénon maps offers the potential of combining ideas from transcendental dynamics in one variable and the dynamics of polynomial Hénon maps in two. Here we show that these maps all have infinite topological and measure theoretic entropy. The proof also implies the existence of infinitely many periodic orbits of any order greater than two

Dynamics of transcendental Hénon maps-II

Transcendental Hénon maps are the natural extensions of the well investigated complex polynomial Hénon maps to the much larger class of holomorphic automorphisms. We prove here that transcendental Hénon maps always have non-trivial dynamical behavior, namely that they always admit both periodic and escaping orbits, and that their Julia sets are non-empty and perfect

Gravity duals of 2d supersymmetric gauge theories

We find new supergravity solutions generated by D5-branes wrapping a four-cycle and preserving four and two supersymmetries. We first consider the configuration in which the fivebranes wrap a four-cycle in a Calabi-Yau threefold, which preserves four supersymmetries and is a gravity dual to the Coulomb branch of two-dimensional gauge theories with N=(2,2) supersymmetry. We also study the case of fivebranes wrapping a co-associative four-cycle in a manifold of G_2-holonomy, which provides a gravity dual of N=(1,1) supersymmetric Yang-Mills theory in two dimensions. We also discuss the addition of unquenched fundamental matter fields to these backgrounds and find the corresponding gravity solutions with flavor brane sources.Comment: 34 pages + appendices; v2: minor improvement

Conceptual-level evaluation of a variable stiffness skin for a morphing wing leading edge

A morphing leading edge produces a continuous aerodynamic surface that has no gaps between the moving and fixed parts. The continuous seamless shape has the potential to reduce drag, compared to conventional devices, such as slats that produce a discrete aerofoil shape change. However, the morphing leading edge has to achieve the required target shape by deforming from the baseline shape under the aerodynamic loads. In this paper, a conceptual-level method is proposed to evaluate the morphing leading edge structure. The feasibility of the skin design is validated by checking the failure index of the composite when the morphing leading edge undergoes the shape change. The stiffness of the morphing leading edge skin is spatially varied using variable lamina angles, and comparisons to the skin with constant stiffness are made to highlight its potential to reduce the actuation forces. The structural analysis is performed using a two-level structural optimisation scheme. The first level optimisation is applied to find the optimised structural proper- ties of the leading edge skin and the associated actuation forces. The structural properties of the skin are given as a stiffness distribution, which is controlled by a B spline interpolation function. In the second level, the design solution of the skin is investigated. The skin is assumed to be made of variable stiffness composite. The stack sequence of the composite is optimised element-by-element to match the target stiffness. A failure criterion is employed to obtain the failure index when the leading edge is actuated from the baseline shape to the target shape. Test cases are given to demonstrate that the optimisation scheme is able to provide the stiffness distribution of the leading edge skin and the actuation forces can be reduced by using a spatially variable stiffness skin

Cooling Strategies for Heated Cylinders Using Pulsating Airflow with Different Waveforms

Pulsate flow is an effective technique applied for cooling several engineering systems depending on their pulsate frequency. One very sound external flow pulsation application is heat transfer over heated bodies. In present work, an experimental design and numerical model of controlled pulsating flow according to generated pulsating frequency and wave shape around a heated cylinder were performed. The effects of pulsating frequency, amplitude, and mean velocity on the fluid flow and heat transfer characteristics over a heated cylinder were studied. The wave frequency varied from 2 to 12 Hz, and the amplitude varied from 0.2 to 0.8 m/s. Moreover, different waveforms were investigated to determine their effect on wall cooling. For constant wave frequency and amplitude, the most efficient wave in cooling was the sawtooth wave, with the average wall temperature after 30 s was 1.6 °C cooler than that of the forced convection case, followed by the triangular wave at 1.2 °C less. The heat transfer rate and the flow field were drastically influenced by the variations of these parameters. Optimization was conducted for each wave type to find the optimum wave frequency and amplitude. The optimizing showed that, the most efficient wave was the sawtooth with 12°C temperature reduction compared with that of the forced convection case, followed by the triangular. Furthermore, regression analysis was conducted to estimate the relationships between these variables and surface temperature. It was found that the wave amplitude had a greater role in cooling than that of the frequency

Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks

In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approaches attempt to predict hard disk failures using either classic machine learning solutions, such as Random Forests (RFs) or deep Recurrent Neural Networks (RNNs). In this work, we address hard disk failure prediction using Temporal Convolutional Networks (TCNs), a novel type of deep neural network for time series analysis. Using a real-world dataset, we show that TCNs outperform both RFs and RNNs. Specifically, we can improve the Fault Detection Rate (FDR) of ≈ 7.5% (FDR = 89.1%) compared to the state-of-the-art, while simultaneously reducing the False Alarm Rate (FAR = 0.052%). Moreover, we explore the network architecture design space showing that TCNs are consistently superior to RNNs for a given model size and complexity and that even relatively small TCNs can reach satisfactory performance. All the codes to reproduce the results presented in this paper are available at https://github.com/ABurrello/tcn-hard-disk-failure-prediction