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Many social robots deployed in public spaces hide hybrid cognitive architectures for dealing with daily tasks. Mostly, two main blocks sustain these hybrid architectures for robot behavior generation: deliberative and behavioral-based mechanisms. Robot Operating System offers different solutions for implementing these blocks, however, some issues arise when both are released in the robot. This paper presents a software engineering approach for normalizing the process of integrating them and presenting them as a fully cognitive architecture named MERLIN. Providing implementation details and diagrams for established the architecture, this research tests empirically the proposed solution using a variation from the challenge defined in the SciRoc @home competition. The results validate the usability of our approach and show MERLIN as a hybrid architecture ready for short and long-term tasks, showing better results than using a by default approach, particularly when it is deployed in highly interactive scenarios.

This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up Robust Features) SURF’s hessian detector into the SIFT algorithm so as to boost the total number of true matched pairs. This is a critical requirement in image processing and widely used in various corresponding fields from image stitching to object recognition. The proposed hybrid algorithm has been tested under different experimental conditions and results are quite encouraging in terms of obtaining higher matched pairs and precision score.

Objective. Learning-based histopathology image (HI) classification methods serve as important tools for auxiliary diagnosis in the prognosis stage. However, most existing methods are focus on a single target cancer due to inter-domain differences among different cancer types, limiting their applicability to different cancer types. To overcome these limitations, this paper presents a high-performance HI classification method that aims to address inter-domain differences and provide an improved solution for reliable and practical HI classification. Approach. Firstly, we collect a high-quality hepatocellular carcinoma (HCC) dataset with enough data to verify the stability and practicability of the method. Secondly, a novel dual-branch hybrid encoding embedded network is proposed, which integrates the feature extraction capabilities of convolutional neural network and Transformer. This well-designed structure enables the network to extract diverse features while minimizing redundancy from a single complex network. Lastly, we develop a salient area constraint loss function tailored to the unique characteristics of HIs to address inter-domain differences and enhance the robustness and universality of the methods. Main results. Extensive experiments have conducted on the proposed HCC dataset and two other publicly available datasets. The proposed method demonstrates outstanding performance with an impressive accuracy of 99.09% on the HCC dataset and achieves state-of-the-art results on the other two public datasets. These remarkable outcomes underscore the superior performance and versatility of our approach in multiple HI classification. Significance. The advancements presented in this study contribute to the field of HI analysis by providing a reliable and practical solution for multiple cancer classification, potentially improving diagnostic accuracy and patient outcomes. Our code is available athttps://github.com/lms-design/DHEE-net., (© 2023 Institute of Physics and Engineering in Medicine.)

Plants are widely grown around the world and have high economic benefits. plant leaf diseases not only negatively affect the healthy growth and development of plants, but also have a negative impact on the environment. While traditional manual methods of identifying plant pests and diseases are costly, inefficient and inaccurate, computer vision technologies can avoid these drawbacks and also achieve shorter control times and associated cost reductions. The focusing mechanism of Transformer-based models(such as Visual Transformer) improves image interpretability and enhances the achievements of convolutional neural network (CNN) in image recognition, but Visual Transformer(ViT) performs poorly on small and medium-sized datasets. Therefore, in this paper, we propose a new hybrid architecture named FOTCA, which uses Transformer architecture based on adaptive Fourier Neural Operators(AFNO) to extract the global features in advance, and further down sampling by convolutional kernel to extract local features in a hybrid manner. To avoid the poor performance of Transformer-based architecture on small datasets, we adopt the idea of migration learning to make the model have good scientific generalization on OOD (Out-of-Distribution) samples to improve the model's overall understanding of images. In further experiments, Focal loss and hybrid architecture can greatly improve the convergence speed and recognition accuracy of the model in ablation experiments compared with traditional models. The model proposed in this paper has the best performance with an average recognition accuracy of 99.8% and an F1-score of 0.9931. It is sufficient for deployment in plant leaf disease image recognition., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hu, Jiang, Zeng, Cheng and He.)

Human activities related to the production and consumption of energy are negatively impacting the environment and need to be addressed by focusing on cleaner production. With this perspective, this study investigates sustainable energy consumption in the industrial and mining sectors. Although these sectors are experiencing exponential data growth, their adoption of big data technologies is limited. Hence, big data methods were applied to design a big data system that makes novel use of microservices and containers for modularity and extensibility. A hybrid architecture incorporating characteristics from polyglot persistence, data lakes and Lambda architecture was used. MongoDB served as the primary data store. The system was implemented for an engineering services company. The implementation yielded seven case studies, which illustrate the impact of big data on decision-making at the various levels of the hierarchy within an organisation. Valuable insights were delivered to personnel at the different levels of the organisational hierarchy. By developing these insights, directed at the appropriate personnel, improved decisions were taken and suitable actions initiated to achieve sustainable energy consumption reductions. The overall impact of the case studies was an energy saving of 75 622 MWh, a reduction of 74 111 Mg of carbon-dioxide-emission, and a saving of 108 141 m 3 of water. Sustained reductions in energy consumption result in cleaner production and will eventually aid in addressing the various energy challenges confronting the economy. [ABSTRACT FROM AUTHOR]

The development of low-cost and highly efficient hydrogen evolution reaction (HER) catalysts has driven continuous effort. Here we develop a one-step preparation method of novel two-dimensional coupled hybrid of molybdenum carbide embedded N-doped carbon nanolayer architecture (MoCx-N/CNL) using cheap cotton as carbon and nitrogen precursor and ammonium molybdate as Mo precursor. Transmission electron microscopy (TEM), high resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED), energy dispersive X-ray spectra (EDS), scanning electron microscopy (SEM), element mapping, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) were utilized to characterize the as-prepared nanocomposites. The synergistic effect between dispersive Mo 2 C particles and N doping in MoCx-N/CNL leads to remarkable HER performance in acidic media. Different amount of Mo were doped in the samples by arranging the ammonium molybdate concentration. Three samples were obtained and MoCx-N/CNL-3 showed better HER electrocatalytic performance than other two samples. Moreover, the as prepared MoCx-N/CNL-3 exhibits excellent stability and durability in acidic media. A new viable route is provided to prepare molybdenum carbide embedded N-doped carbon hybrid architecture with higher HER stability and activity, it is of significance in novel HER catalyst preparation. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of collision avoidance of ground vehicles. In this framework, they focus on a single vehicle, and they guarantee that a given safe set is not exited during a mission. More specifically, they consider the yaw‐lateral motion of a two‐wheel vehicle subject to a side wind, and they design a state‐feedback controller such that the vehicle follows the nominal trajectory with a given maximum error. For these purposes, they resort to a control system where a supervisor, implemented as a hybrid automata, decides to switch between two controllers, depending on the measured vehicle lateral displacement from the nominal trajectory. The two controllers are designed using an H∞ approach and the input–output finite‐time stability approach, respectively. Some simulation results are included, showing the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

Abstract: The complexities and functionalities of carbon nanotube (CNT) architectures can be enhanced even further by the addition of nanoparticles of different kinds. Here, a simple and easily scalable energy efficient infiltration technique is demonstrated to incorporate iron oxide (Fe3O4) particles into aligned forests of CNTs grown by chemical vapor deposition. Scanning electron microscopy and in situ confocal microscopy confirm the presence of Fe3O4 and also explain the mechanism of infiltration and entrapment in the nanotube films. The obtained hybrid films are demonstrated to be excellent field emitters. The infiltration of nanoparticle results in an order of magnitude improvement in the turn‐on field, which can be attributed to several advantageous factors such as reduced screening effects, improved conductivity, and local electric field enhancement in the proximity of the particles. The present method is generic and thus can be applied to other magnetic particles and porous host materials aiming at innovative sensor, electrical and environmental applications. [ABSTRACT FROM AUTHOR]

Insights within thermal expansion, conductivity, and decomposition dependencies with temperature on symmetrical and unsymmetrical layered carbon (CF) or basalt (BF) fabrics in combination with flax fibers (FF) were approached. Driven by commercial application and environmental concerns, the paper draws attention on a modified formula of cyanate ester with a common epoxy resin under an optimized ratio of 70:30 (vol%) as well as on the hybrid reinforcements stacking sequences. Synergetic effects were debated in terms of the CF and BF stacking sequences and corresponding volume fraction followed by comparisons with values predicted by the deployment of hybrid mixtures rules (RoHM/iRoHM). CF hybrid architectures revealed enhanced effective thermophysical properties over their BF counterparts and both over the FF-reinforced polymer composite considered as a reference. Thermal conductivities spread between 0.116 and 0.299 W m−1 K−1 from room temperature up to 250 °C on all hybrid specimens, giving rise to an insulator character. Concerning the coefficient of thermal expansion, CF hybrid architectures disclosed values of 1.236 10−6 K−1 and 3.102 10−6 K−1 compared with BF affine exhibiting 4.794 10−6 K−1 and 6.245 10−6 K−1, respectively, with an increase in their volume fraction. [ABSTRACT FROM AUTHOR]

Modern heterogeneous computing platforms have become powerful HPC solutions, which could be applied to a wide range of real-life applications. In particular, the hybrid platforms equipped with Intel Xeon Phi coprocessors offer the advantages of massively parallel computing, while supporting practically the same parallel programming model as conventional homogeneous solutions. However, there is still an open issue as to how scientific applications can efficiently utilize hybrid platforms with Intel MIC coprocessors. In this article, we propose an approach for porting a real-life scientific application to such hybrid platforms, assuming no significant modifications of the application code. It allows us to take advantage of all the computing components, including two CPUs and two coprocessors, for the parallel execution of computational workloads. In this study, we focus on the parallel implementation of a numerical model of the dendritic solidification process in isothermal conditions. We develop a sequence of steps that are necessary for the porting and optimization of the solidification application to hybrid platforms with Intel coprocessors. The main challenges include not only overlapping data movements with computations, but also ensuring adequate utilization of cores/threads and vector units of processors, as well as coprocessors. To reach this aim, we propose an efficient and flexible method for the workload distribution between heterogeneous computing components. For implementing the potential benefits of the proposed approach, we choose a heterogeneous programming model based on a combination of the offload mode for Intel MIC and OpenMP programming standard. The developed approach allows us to execute the whole application up to 9.33× faster than the original parallel version that uses two CPUs. Furthermore, the CPU–MIC hybrid platforms enable achieving the speedup of about 1.9× that of the CPU platform with 24 cores based on the Ivy Bridge architecture, and about 1.5× that of the Haswell-based CPU platform with 36 cores. [ABSTRACT FROM AUTHOR]

The deeper reasons of the present stalling in computing is scrutinized, and to enhance the single-processor performance, a new approach explicitly considering the presence of several computing units is introduced, as opposed to the presently exclusively used, 70-years old single-processor approach. The appearance of many-core processors, having many processing units in close vicinity to each other, requires to re-think some principles of computing. The goal of the approach is to enhance the single-processor performance using cooperating cores, rather than to introduce a new method for parallelization. Technically, it introduces a new control layer above the cores, a new intermediate execution unit called quasi-thread, a modified compiling method and object code for transferring parallelization information from the development system to the processor, and an on-demand self-organizing processor architecture. The resulting processors have more effective and more “green” architecture, considerably increased single-thread performance, allow for more deterministic real-time behaviour, new scheduling principles for multitasking, less operating system overhead, etc. Surprisingly, the resulting computing stack is upward compatible with the presently existing one. [ABSTRACT FROM AUTHOR]

Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches. [ABSTRACT FROM PUBLISHER]

With the increasing number of vehicles connected to the Internet-of- Things (IoT), Internet-of-Vehicles (IoV) is becoming a hot research topic. It can improve traffic safety and efficiency and promote the development of the intelligent transportation that is a very important element in Smart Cities. As an important part of the safety application in IoV, the emergency message transmission is designed to inform all the vehicles in the relevant area timely of the accident information through the multi-hop broadcast communication. In this paper, we propose a hybrid emergency message transmission (HEMT), which introduces the SDN technology into the vehicular network environment and utilizes the flexibility of intervehicle communication. By deploying SDN-enabled central controller and RSU switches, we can obtain reliable and fast emergency message dissemination. Moreover, considering the space between the coverages of RSUs caused by the sparse deployment, we also use inter-vehicle multi-hop broadcast communication to improve the message coverage ratio by adding the packet modification module on the RSU switch. Simulation results show the feasibility and effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is becoming increasingly popular due to its numerous applications. This is especially evident in games, medicine, education, and other areas that support our everyday activities. Moreover, this kind of computer system not only improves our vision and our perception of the world that surrounds us, but also adds additional elements, modifies existing ones, and gives additional guidance. In this article, we focus on interpreting a reality-based real-time environment evaluation for informing the user about impending obstacles. The proposed solution is based on a hybrid architecture that is capable of estimating as much incoming information as possible. The proposed solution has been tested and discussed with respect to the advantages and disadvantages of different possibilities using this type of vision. [ABSTRACT FROM AUTHOR]

Millimeter Wave (mmWave) Massive Multiple Input Multiple Output (MIMO) has been a promising candidate for the current and next generation of cellular networks. The hybrid analogue/digital precoding will be a crucial ingredient in the mmWave cellular systems to reduce the number of Radio Frequency (RF) chains along with the corresponding energy and power consumption of the systems. In this paper, we aim to improve the energy efficiency of mmWave Massive MIMO by using a combination of high dimension analogue precoder and low dimension digital precoder. The spectral efficiency and the corresponding transmitted and consumed power of the mmWave Massive MIMO is formulated by taking all the consumed power from the transmitting side to receiving end into account. We propose the Power Controlled Energy Maximization (PCEM) algorithm in this paper, and the proposed algorithm works by controlling the transmission power to balance the improved radiated energy efficiency and the increased power consumption for a given number of transceiver chains. The simulation and analytical results show that the proposed algorithm performs better than the reference algorithms by maximizing the overall energy efficiency of the system without much complexity.

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.