Use Of Masking Techniques Research Articles

Strain-mediated reservoir computing with temporal and spatial co-multiplexing in multiferroic heterostructures

Physical reservoir computing (PRC), a brain-inspired computing method known for its efficient information processing and low training requirements, has attracted significant attention. The key factor lies in the number of computational nodes within the reservoir for its computational capability. Here, we explore co-multiplexing reservoirs that leverage both temporal and spatial strategies. Temporal multiplexing virtually expands the node count through the use of masking techniques, while spatial multiplexing utilizes multiple physical locations (e.g., Hall bars) to achieve an increase in the number of real nodes. Our experiment employs a strain-mediated reservoir based on multiferroic heterostructures. By applying a single voltage across the PMN-PT substrate (acting as global input) and measuring the output Hall voltages from four Hall bars (real nodes), we achieve significant efficiency gains. This co-multiplexing approach results in a reduction in the normalized root mean square error from 0.5 to 0.23 for a 20-step prediction task of a Mackey–Glass chaotic time series. Furthermore, the single input and four independent outputs lead to a fourfold reduction in energy consumption compared to the strain-mediated PRC with temporal multiplexing solely. This research paves the way for future energy saving PRC implementations utilizing co-multiplexing, promoting a resource-efficient paradigm in reservoir computing.

Exploring Reproducible Nonaqueous Scanning Droplet Cell Electrochemistry in Model Battery Chemistries.

The discovery and optimization of new materials for energy storage are essential for a sustainable future. High-throughput experimentation (HTE) using a scanning droplet cell (SDC) is suitable for the rapid screening of prospective material candidates and effective variation of investigated parameters over a millimeter-scale area. Herein, we explore the transition and challenges for SDC electrochemistry from aqueous toward aprotic electrolytes and address pitfalls related to reproducibility in such high-throughput systems. Specifically, we explore whether reproducibilities comparable to those for millimeter half-cells are achievable on the millimeter half-cell level than for full cells. To study reproducibility in half-cells as a first screening step, this study explores the selection of appropriate cell components, such as reference electrodes (REs) and the use of masking techniques for working electrodes (WEs) to achieve consistent electrochemically active areas. Experimental results on a Li-Au model anode system show that SDC, coupled with a masking approach and subsequent optical microscopy, can mitigate issues related to electrolyte leakage and yield good reproducibility. The proposed methodologies and insights contribute to the advancement of high-throughput battery research, enabling the discovery and optimization of future battery materials with improved efficiency and efficacy.

Resilient uniformity: applying resiliency in masking

Threshold Implementations are known countermeasures defending against side-channel attacks via the use of masking techniques. While sufficient properties are known to defend against first-order side-channel attacks, it is not known how to achieve higher-order security. This work generalizes the Threshold Implementation notion of uniformity and proves it achieves second-order protection. The notion is applied to create a second-order masking of the Present cipher with a low randomness cost.

Adapting Laplacian based filtering in digital image processing to a retina-inspired analog image processing circuit

In this paper, a unique biologically inspired retina circuit architecture providing Laplacian filter based analog image processing has been suggested. A digital image filtering method is utilized for this aim. Convolution theory and masking technique have an important place among digital image processing methods. These two mathematical operations can be easily done with basic electronic circuit structures. We use current mirrors and current subtractor circuit for the purpose of performing convolution by the use of masking technique on any image. The concept of human retina is able to be mimicked by the help of using silicon circuits. A retina construction can be thought as a group of pixel structures. Because of this reason, we first design a novel pixel circuit as a subcircuit for the retina structure. Our new retina-inspired neuromorphic pixel consists of only 8 MOS transistors. Then, 10 k identical pixel circuits are united together with the help of proper subcircuit connections to achieve a retina structure of size 100 × 100 pixels which enables edge detection feature on images thanks to Laplacian filtering. We compare the analysis results of our grid retina circuit with the theoretical Laplacian filter method used in digital image processing. We obtain analysis results of four different grayscale images that agree well with the expected theoretical results for Laplacian filtering.

Clasificador de imágenes de frutas basado en inteligencia artificial

Las aplicaciones de visión artificial y el análisis de imágenes son capaces de resolver varios problemas del sector industrial, científico o de seguridad en la actualidad. La clasificación de imágenes es muy útil en la automatización de procesos en una empresa. Para realizar una tarea de clasificación de imágenes se requiere hacer la extracción de características que identifiquen a cada tipo de imagen como por ejemplo: color, forma, textura. En el presente trabajo se requiere implementan los algoritmos para la construcción de un clasificador de imágenes de frutas basado en la extracción de las características del color de las imágenes en determinadas regiones de interés. Para el desarrollo del clasificador de imágenes de frutas se utiliza la técnica de extracción del histograma a color en tres dimensiones y con la implementación de algoritmos de inteligencia artificial se efectúa la clasificación automática de imágenes. El conjunto de datos utilizado consiste en: cuatro clases de frutas con el número variable de imágenes por cada clase, luego se preparan las imágenes seleccionando las regiones de interés mediante técnicas de enmascaramiento y se las divide en dos grupos de datos: Los datos de entrenamiento y los datos de prueba. Luego de entrenado el clasificador, se realizan pruebas de clasificación para evaluar la eficacia del clasificador de imágenes de frutas. Esta metodología de construcción e implementación del clasificador se puede usar en varias aplicaciones según las clases de imágenes de objetos a analizar en casos similares y automatizar procesos de clasificación y reconocimiento de objetos.