High-speed Computer Research Articles

Emergence of Weyl points and large anomalous Hall conductivity in layered Bi2TeMnI2.

In recent years, narrow band gap layered materials were reported as an interesting candidate for energy efficient devices. Here, we chose BiTeI, a layered material that has significant Rashba spin splitting, for charge modification with the purpose of exploring the electronic, magnetic and topological properties. Chemical doping with an Mn atom is done to the Te site in BiTeI. On the basis of density functional theory calculations, we found that the parent material BiTeI is a semiconductor with an indirect band gap of ∼0.46 eV within full-relativistic mode. The orbital contributions around the Fermi level are found to be mainly from the Bi-6p, I-5p and Te-5p states in the electronic structure. Upon chemical doping by Mn to Bi, Te and I separately, doping to the Te site is energetically favorable with a ferromagnetic ground state and a semimetallic behaviour. The doped material, i.e., Bi2TeMnI2, is found to be a magnetic Weyl semimetal with six Weyl points close to the Fermi level (around 100 meV in the conduction region). Our calculations suggest Bi2TeMnI2 as a probable candidate of a Weyl semimetal. The emergence of Weyl points gives rise to a large intrinsic anomalous Hall conductivity of up to ∼750 Ω-1 cm-1. The calculated negative value of formation energy (-0.233 eV) and the positive phonon frequency suggests Bi2TeMnI2 to be thermodynamically favorable and dynamically stable. This work deserves a transport experiment to confirm our claim, which might provide insights towards discovering new quantum materials suitable for high-speed electronics, spintronics and quantum computing.

Fault Current Unified Calculation Method for Whole Process Fault Ride-Through of DFIG-Based Wind Farms

The present inaccurate fault current calculation, due to the incomplete consideration of whole process operation stages of fault ride-through (FRT) of DFIG-based wind farms (WFs), has a distinct impact on the safe operation of local power grid. To tackle this challenge, a fault current unified calculation (FCUC) method is proposed to achieve high-accuracy and high-speed mathematical analytical calculations of DFIGs output fault current during FRT. The proposed FCUC method mainly includes: 1) Whole process unified state space model, which contains transition stage, crowbar protection stage, demagnetization stage, reactive current compensation stage, and dc-chopper protection stage, is established to thoroughly consider FRTs different internal protection circuits and control strategies; 2) Stator output fault current is conducted by the unified state space model based on the stator residual voltage and the stator flux linkage; 3) Grid side converter (GSC) output fault current, which considers both injection mode and absorption mode, is developed based on the grid residual voltage and the unified state space model; 4) Fault current contribution mechanism, which is aggregated from both stator and grid side converter, is revealed based on Laplace transform from time-frequency-time domain, and is first expressed as an unified fault current expression. Extensive simulation/experimental/calculation results under different voltage sags have validated the correctness and effectiveness of the proposed FCUC method.

An investigation into connection between BIM and Digital Twins technologies

With the evolution of high-speed computing, 5G mobile communication, artificial intelligence, a rapid growth of Industry 4.0 is expected, and there is now a push towards Industry 5.0 (I5.0). However, I5.0 will require a lot of integration and interaction between humans, machines, big data and Artificial Intelligence (AI) in a very complex spider-net like intercommunication. Building infrastructure will play an important role intercity life, and to maintain cities’ viability Digital Twin will be the gateway of development, implementation and monitoring. Microsoft Azure Digital Twins is studied as a possible and cost-effective Cloud-Based Digital Twin solution with real time data exchange and interaction between a physical system and its Digital Twin. Azure Digital Twins was chosen as commercially available software platform as a service, in contrast to other similar solutions implemented as software as a service solutions.

MEConformer: Highly representative embedding extractor for speaker verification via incorporating selective convolution into deep speaker encoder

Transformer models have demonstrated superior performance across various domains, including computer vision, natural language processing, and speech recognition. The success of these models can be attributed to their robust parallel capacity and high computation speed, primarily reliant on the attention layer. In the domain of speaker recognition, state-of-the-art results have been achieved using convolutional neural network (CNN) architectures, particularly with speaker embeddings represented by x-vectors and r-vectors. However, existing CNN-based methods tend to focus on local features while overlooking the global dependence of voiceprint features, resulting in the loss of crucial information. Moreover, the presence of noise in audio data is an influential factor that cannot be disregarded, as it significantly impacts the extraction of discriminative speaker embeddings. To address these challenges, we propose a novel model called the Multi-Scale Expand Convolution Transformer (MEConformer). This model aims to convert variable-length audio into a fixed low-dimensional representation. The MEConformer leverages a CNN framework with expanded receptive fields to capture frame-level features effectively. Additionally, we introduce a transformer encoder that incorporates contextual dependencies, enabling the extraction of both frame-level and discourse-level feature representations. Furthermore, we present a multi-scale residual aggregation strategy, which facilitates the efficient transmission of voiceprint information across the model. By combining these innovative components, the MEConformer achieves a state-of-the-art Equal Error Rate (EER) of 3.72% on the VoxCeleb1 test set. Furthermore, it demonstrates EERs of 5.94% and 3.72% on the VoxCeleb1-H and VoxCeleb-E datasets, respectively. The code for the proposed MEConformer model will be made publicly available at https://codeocean.com/capsule/4563012/tree.

HOW TECHNOLOGY IMPACTS LONELINESS: PERSPECTIVES OF AFRICAN AMERICAN OLDER ADULTS

Abstract The use of information and communication technologies (ICT) has been found to decrease loneliness among older adults by enabling communication with friends and family. Over 25% of older adults experience loneliness, and having a larger social network can reduce this risk (Chawla et al., 2021; Dahlberg et al., 2022). Despite the success of using ICTs and the growing ubiquity of ICTs among people of varying social demographic backgrounds there remains a digital divide, which refers to inequality in terms of access to ICT based on race, age, economic status, or other factors. While ICT use is becoming increasingly common among older adults, few studies have included African American older adults, who are less likely to have access to high-speed internet and computers than their White counterparts. However, African American adults are more likely to use text messages, video and voice calls, email, and social media to stay connected. The proposed research aims to explore the perspectives of African American older adults on how ICT impacts loneliness. Semi-structured interviews will be used to collect data from a purposive sample of about 10 people who meet specific selection criteria. Data will be analyzed using thematic analysis. The results may be useful for gerontological practitioners interested in promoting ICT use as a way to enhance the social well-being of African American older adults. The study could lead to the development of interventions that increase technology adoption and use, ultimately reducing loneliness among African American older adults.

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis.

Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models.

A novel hedge algebra formation control for mobile robots

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra’s significant potential in the mobile robots’ domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain.

High-speed parallel processing with photonic feedforward reservoir computing.

High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time-delayed feedback loops and use analog-to-digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non-feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully-analog feed-forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength-division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing.

Automated Backend Allocation for Multi-Model, On-Device AI Inference

On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for execution on CPU and GPU computing backends. While GPU delegation is a common practice to achieve high speed computation, the approach suffers from degraded throughput and completion times under multi-model scenarios, i.e. concurrently executing services. This paper introduces a solution to sustain performance in multi-model, on-device AI contexts by dynamically allocating a combination of CPU and GPU backends per model. The allocation is feedback-driven, and guided by a knowledge of model-specific, multi-objective pareto fronts comprising inference latency and memory consumption. Primary contribution of this paper is a backend allocation algorithm that runs online per model, and achieves 25-100% improvement in throughput over static allocations as well as load-balancing scheduler solutions targeting multi-model scenarios. Other noteworthy contributions include a novel pareto front estimator that runs on-device, and also a software-based GPU profiler with a lightweight algorithm to detect changing GPU workloads. Specifically, the pareto front estimator outperforms state of the art algorithms NSGA-II and SPEA2 by 94% on pareto coverage, and by almost 2x on computational overhead.

Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm.

In recent days, cyber-physical systems (CPS) have become a new wave generation of human life, exploiting various smart and intelligent uses of automotive systems. In these systems, information is shared through networks, and data is collected from multiple sensor devices. This network has sophisticated control, wireless communication, and high-speed computation. These features are commonly available in CPS, allowing multi-users to access and share information through the network via remote access. Therefore, protecting resources and sensitive information in the network is essential. Many research works have been developed for detecting insecure networks and attacks in the network. This article introduces a framework, namely Deep Bagging Convolutional Neural Network with Heuristic Multiswarm Ant Colony Optimization (DCNN-HMACO), designed to enhance the secure transmission of information, improve efficiency, and provide convenience in Cyber-Physical Systems (CPS). The proposed framework aims to detect attacks in CPS effectively. Compared to existing methods, the DCNN-HMACO framework significantly improves attack detection rates and enhances overall system protection. While the accuracy rates of CNN and FCM are reported as 72.12% and 79.56% respectively, our proposed framework achieves a remarkable accuracy rate of 92.14%.

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method.

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.

A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection

With the increasing use of onboard high-speed computing systems, vehicle manufacturers are offering significant advanced features of driver assistance systems. Pedestrian detection is one of the major requirements of such systems, which commonly use cameras, radar, and ultrasonic sensors. Image recognition based on captured image streams is one of the powerful tools used for the detection of pedestrians, which exhibits similarities and distinguishing features compared to general object detection. Although pedestrian detection has advanced significantly along with deep learning, some issues still need to be addressed. Pedestrian detection is essential for several real-world applications and is an initial step in outdoor scene analysis. Typically, in a crowded situation, conventional detectors are unable to distinguish persons from each other successfully. This study presents a novel technique, based on the Dual Gate Mixed Dilated Convolution Network, to address this problem by adaptively filtering spatial areas where the patterns are still complicated and require further processing. The proposed technique manages obscured patterns while offering improved multiscale pedestrian recognition accuracy.

Analytical Double-2D Frequency-Dependent Leakage Inductance Model for Litz-Wired High-Frequency Transformer

Accurate and fast calculation of frequency-dependent leakage inductance is critical for the optimization of the high-frequency transformer with Litz wire winding. The analytical method is widely used due to its high computation speed. However, its computation accuracy is limited due to the following factors. i) One-dimensional (1D) or two-dimensional magnetic field model (2D) has unsatisfying accuracy for the transformer with a rotationally asymmetric structure. ii) The skin and proximity effect factors based on the multilayer foil winding are inaccurate for the Litz wire winding in the calculation of frequency-dependent magnetic energy. This paper proposes a double-2D frequency-dependent leakage inductance model for the transformer with Litz wire winding. It uses the image method to calculate the 2D leakage magnetic field inside and outside the core window, respectively, which considers the rotationally asymmetric structure in leakage inductance calculation. The round conductor model is proposed for the frequency-dependent magnetic energy calculation. The single strand in a Litz wire is taken as the basic modeling object of the round conductor model, which is more detailed than the multilayer foil winding model for the Litz wire winding. The comparison between the experiment, FEM, state-of-the-art models and the proposed model is conducted for eight transformers with E-core, U-core, round or rectangular Litz wire winding. The proposed model shows satisfying computation accuracy in the whole frequency band. And the computational speed of the proposed model is 100 times that of the FEM.

Patchwork: Alignment-Based Retrieval and Concatenation of Phylogenetic Markers from Genomic Data.

Low-coverage whole-genome sequencing (also known as "genome skimming") is becoming an increasingly affordable approach to large-scale phylogenetic analyses. While already routinely used to recover organellar genomes, genome skimming is rather rarely utilized for recovering single-copy nuclear markers. One reason might be that only few tools exist to work with this data type within a phylogenomic context, especially to deal with fragmented genome assemblies. We here present a new software tool called Patchwork for mining phylogenetic markers from highly fragmented short-read assemblies as well as directly from sequence reads. Patchwork is an alignment-based tool that utilizes the sequence aligner DIAMOND and is written in the programming language Julia. Homologous regions are obtained via a sequence similarity search, followed by a "hit stitching" phase, in which adjacent or overlapping regions are merged into a single unit. The novel sliding window algorithm trims away any noncoding regions from the resulting sequence. We demonstrate the utility of Patchwork by recovering near-universal single-copy orthologs within a benchmarking study, and we additionally assess the performance of Patchwork in comparison with other programs. We find that Patchwork allows for accurate retrieval of (putatively) single-copy genes from genome skimming data sets at different sequencing depths with high computational speed, outperforming existing software targeting similar tasks. Patchwork is released under the GNU General Public License version 3. Installation instructions, additional documentation, and the source code itself are all available via GitHub at https://github.com/fethalen/Patchwork.

Simulation of supersonic jet flow past a blunt body in a laboratory experiment using computer vision

The complex flow of a supersonic jet (Mach number 2.4–2.5) around the blunt aerodynamic body (consisting of a cylinder with a diameter of 16 mm and a truncated cone) was studied using the high-speed shadowgraph technique and computer vision-based visual data processing. The problem considered is directly related to the rocket and space systems stages safe separation problem in a sufficiently dense atmosphere. For systems with a sequential arrangement of stages, their separation often occurs under the rocket engine jet action. The occurrence of lateral forces in violation of axial symmetry can lead to overturning of the discarded stage and undesirable interaction with the outgoing stage. The goals of the paper were to study the pulsation characteristics of the incoming supersonic jet streamlining the body, as well as the pulsations of the bow shock wave in both axisymmetric and asymmetric flows. The recording frame rate in the experiments was up to 775 000 fps. Computer vision and various machine learning approaches were used to process a large number of shadowgraph images. Canny edge detection and Hough transform, as well as a convolutional neural network (CNN) based on YOLOv2 architecture, were used to automatically track the position of the bow shock relative to the body. Jet sound generation was studied - the amplitude dependence on the distance from the nozzle and the evolution in time. The position of the bow shock relative to the streamlined body and flow pulsations over time were analyzed. Bow shock pulsation spectra at zero angle of attack were compared to those at a small angle of attack (α = 2.5°–3°). The power spectral density was also estimated. The slope of the spectra for the zero angle of attack case was found to be horizontal and the spectra for the small angle of attack case had a slope close to −5/3. The strongest of the measured bow shock frequencies were found to be close to those of the jet mixing layer pulsations. The hardware signal spectrum was also measured and this signal can be considered as noise. The described approach makes it possible to obtain the physical characteristics of the flow at any point of interest. It was also shown that the automation of flow visualization experiments using computer vision techniques allows to increase the speed of data processing and obtaining new physical information, which may be important for engineers developing aircraft and spacecraft technologies.

A new scheme of 2:1 photonic multiplexer and multiplexer-based NOT, OR, AND logic gates in electro-optic Mach–Zehnder interferometer

This paper introduces the development of NOT, OR, and AND logic gates utilizing a 2:1 Multiplexer (MUX) based on a titanium-diffused lithium niobate electro-optic Mach–Zehnder interferometer. The shift towards large-scale optical integrated circuits as an alternative to traditional CMOS technology has gained traction, driven by the growing demand for high-speed computation and data transfer rates, reaching up to the terahertz scale, along with the need for energy-efficient interconnects. To optimize the design and reduce the number of photonic MUX, we leverage Shannon decomposition and Reduced Binary Decision Diagram mapping in the creation of MUX-based combinational and logic circuits. The entire design undergoes comprehensive simulation and verification using OPTIBPM with a beam propagation method (BPM). The wafer dimensions for the 2:1 electro-optic MZI-MUX and MUX-based NOT, AND, OR gates are 33 mm × 100 μm, classifying them as integrated optics. The proposed photonic MUX-based combinational circuits exhibit rapid response times, making them particularly advantageous for communication systems, transmission networks, and various industrial applications. Key device parameters, including insertion loss (0.0012 dB), extinction ratio (ER) (28.97 dB), and contrast ratio (29.5 dB), fall within acceptable limits. The BPM simulated results align well with the mathematical computations based on PYTHON simulations.

Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

A new fast ADMM for kernelless SVM classifier with truncated fraction loss

Support vector machine (SVM) is one of well-known supervised machine learning classifier and is used widely in image classification, pattern recognition, disease diagnosis, etc. However, the high computational complexity is a major issue for large-scale classification problems. To address this issue, we propose a new truncated fraction loss function to construct a new sparse and robust SVM model (Ltf-SVM). By newly developed support vectors and a working set based on the proximal operator of truncated fraction loss, we prove the sparsity and robustness to outliers of Ltf-SVM from a theoretical perspective. When it comes to solve Ltf-SVM problems, we develop an alternating direction method of multipliers with a working set for addressing Ltf-SVM. The proposed algorithm is proven to converge globally and enjoys very low computational complexity. Moreover, numerical experiments verify its superior performance with respect to its high accuracy and high computational speed in comparison with other leading solvers.

Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature

AbstractFine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.