Photonic Cavity Effects for Enhanced Efficiency in Layered Perovskite-Based Light-Emitting Diodes

Layered architectures for light-emitting diodes (LEDs) are the standard approach for solution-processable materials such as metal-halide perovskites. Upon designing the composition and thicknesses of the layers forming the LED, the primary focus is typically on the optimization of charge injection and balance. However, this approach only considers the process until electrons and holes recombine to generate photons, while for achieving optimized LED performance, the generated light must also be efficiently outcoupled. Our work focuses on the latter aspect. We assume efficient photon generation and analyze the effects of the geometrical configuration together with the dipole orientation, mimicking the light emission, on the main characteristics defining the LED, such as the Purcell effect and the outcoupling efficiency. We find that in-plane dipoles result in significantly increased outcoupling efficiency. Furthermore, the mismatch in refractive index among the layers and their different thicknesses can be tuned to maximize the Purcell effect and minimize internal losses. The combined optimization of dipole orientation and layer thicknesses can improve the efficiency of the LED up to a factor 10, hence highlighting the importance of considering also the photonic properties of the LED structures if the objective is to maximize the LED performance.

Sulfidation-Oxidation Resistance of Thermal Diffusion Multi-Layered Coatings on Steels

The high-temperature sulfidation-oxidation corrosion resistance of protective coatings deposited on carbon and 316L steels was studied. The coatings obtained via the thermal diffusion process had multi-layered architectures and consisted of aluminides, iron borides, or iron boride–TiO2 layers. The protective coatings experienced a minimal rate of mass changes, insignificant scale formation, and no delamination and surface micro-cracking after 504 h of exposure in 1% (Vol.) H2S-air atmosphere at 500 °C. Furthermore, the coatings demonstrated a high degree of integrity compared to bare 316L stainless steel. Aluminized steels demonstrated the highest performance among the studied materials. The developed thermal diffusion coatings are promising candidates due to their enhanced stability in H2S–air atmosphere; they may be employed for protection of inner and outer surfaces of long tubing and complex shape components.

Internet of Things: A Review of Functionality, Applications, Architectures and Challenges

The Internet of Things (IoT) is an emerging paradigm that embodies the vision of merging smart objects while utilizing the internet as the backbone of the communication system to establish a smart interaction among physical entities in pervasive environments. In IoT, data is generated in realtime and stored in permanent repositories. Additional data in the form of meta-data that describes things adds to the data volume and to manage this data, architecture is required. IoT finds its applicability in a plethora of applications such as transportation, smart city, smart health, smart environment, home entertainment, sports, etc. but there is no universal architecture for all IoT implementations. We have layered architectures and domain-specific architectures for different IoT applications. Besides a large number of architectures for IoT, it faces several potential challenges such as scalability, reliability, heterogeneity, etc. This paper presents an understanding of the Internet of Things in terms of its functionality, layered and domain-specific architectures, and its potential challenges.

Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control

Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed–accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops—a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow—each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create “diversity-enabled sweet spots,” so that both fast and accurate control is achieved using slow or inaccurate components.

Complex Computation from Developmental Priors

Artificial Inteligence (AI) research has provided key insights into the mechanics of learning complex tasks. However, AI models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Although innate neural solutions have inspired AI approaches from layered architectures to ConvNets, the underlying neuroevolutionary search for novel heuristics has not been succesfully systematized. In this manuscript, we examine how neurodevelopmental principles can inform the discovery of computational heuristics. We begin by considering the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network's weights directly, we improve task fitness by updating the neurons' wiring rules, thereby mirroring evolutionary selection on brain development. We find that the resulting framework can not only achieve high performance on standard machine learning tasks, but does so with a fraction of the full network's parameters. When we condition neuronal identity on biologically-plausible spatial constraints, we discover weight structures that resemble visual filters while further compressing parameters count. In summary, by introducing realistic developmental considerations into machine learning frameworks, we not only capture the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations.

Recent advances on hybrid integration of 2D materials on integrated optics platforms

The burgeoning research into two-dimensional (2D) materials opens a door to novel photonic and optoelectronic devices utilizing their fascinating electronic and photonic properties in thin-layered architectures. The hybrid integration of 2D materials onto integrated optics platforms thus becomes a potential solution to tackle the bottlenecks of traditional optoelectronic devices. In this paper, we present the recent advances of hybrid integration of a wide range of 2D materials on integrated optics platforms for developing high-performance photodetectors, modulators, lasers, and nonlinear optics. Such hybrid integration enables fully functional on-chip devices to be readily accessible researchers and technology developers, becoming a potential candidate for next-generation photonics and optoelectronics industries.

Combining Low-Level Image Features with Features from A Simple Convolutional Neural Network

In the traditional image processing approaches, first low-level image features are extracted and then they are sent to a classifier or a recognizer for further processing. While the traditional image processing techniques employ this step-by-step approach, majority of the recent studies prefer layered architectures which both extract features and do the classification or recognition tasks. These architectures are referred as deep learning techniques and they are applicable if sufficient amount of labeled data is available and the minimum system requirements are met. Nevertheless, most of the time either the data is insufficient or the system sources are not enough. In this study, we experimented how it is still possible to obtain an effective visual representation by combining low-level visual features with features from a simple deep learning model. As a result, combinational features gave rise to 0.80 accuracy on the image data set while the performance of low-level features and deep learning features were 0.70 and 0.74 respectively.

Architecting and Deploying IoT Smart Applications: A Performance–Oriented Approach

Layered internet of things (IoT) architectures have been proposed over the last years as they facilitate understanding the roles of different networking, hardware, and software components of smart applications. These are inherently distributed, spanning from devices installed in the field up to a cloud datacenter and further to a user smartphone, passing by intermediary stages at different levels of fog computing infrastructure. However, IoT architectures provide almost no hints on where components should be deployed. IoT Software Platforms derived from the layered architectures are expected to adapt to scenarios with different characteristics, requirements, and constraints from stakeholders and applications. In such a complex environment, a one-size-fits-all approach does not adapt well to varying demands and may hinder the adoption of IoT Smart Applications. In this paper, we propose a 5-layer IoT Architecture and a 5-stage IoT Computing Continuum, as well as provide insights on the mapping of software components of the former into physical locations of the latter. Also, we conduct a performance analysis study with six configurations where components are deployed into different stages. Our results show that different deployment configurations of layered components into staged locations generate bottlenecks that affect system performance and scalability. Based on that, policies for static deployment and dynamic migration of layered components into staged locations can be identified.