Rapid Development Of Hardware Research Articles

Quantum Computing for High-Energy Physics: State of the Art and Challenges

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers. Published by the American Physical Society 2024

Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates

Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.

Research and Analysis of Ray Tracing Methods

In computer graphics, the study of how to build realistic scenes on the computer has been the focus of research. The dominant approach to simulating the correct light source was to use rasterization, but rasterization does not correctly simulate multiple reflections. To make virtual scenes more realistic, ray tracing was proposed to provide better results. With the rapid development of computer hardware, the use of ray tracing for rendering animation or games is becoming more and more common, and many scenes can now be rendered with ray tracing instead of rasterization for better results. This paper introduces and analyses the various methods of ray tracing in chronological order, starting with the original forward ray tracing and backward ray tracing, followed by Whitted algorithms with recursion, and then a series of methods introducing rendering formulas. And then, a few rays tracing algorithms introducing the Metropolis sampling method is mentioned. One can conclude that this method is going to be used for future optimization and development. It is significant for the development of ray tracing.

SSD-Resnet50: Research on Pedestrian Detection Technology

In the era of artificial intelligence, with the rapid development of computer hardware, the defects of neural network algorithm have been further improved, the technology of artificial intelligence is progressing, the algorithm and technology of target detection have been further improved, and pedestrian detection technology is an important research direction of target detection technology. The main research content of this paper consists of three parts: the improvement of the original SSD algorithm, and the fundamental composition and characteristics of the neural network; Changing VGG16, the basis of the original SSD algorithm, to Resnet50 can improve the detection speed and accuracy. A feature fusion module is introduced to fuse the feature map with large receptive field and small receptive field, so as to improve the overall expression ability of the feature map to the image.

QCSH: A full quantum computer nuclear shell-model package

The nuclear system is a promising area for demonstrating practical quantum advantage. A comprehensive computation of a nuclear system in a classical computer is beyond the capacity of current classical computers. With the rapid development of hardware, the prospect of using quantum computers in nuclear physics is close at hand. In this paper, we report a full quantum package, QCSH, for solving a nuclear shell model in a quantum computer. QCSH uses the linear combination of the unitary formalism of quantum computing and performs all calculations in a quantum computer. The complexities of qubit resource and the number of basic gates of QCSH are both polynomials to the number of nucleons in nuclei. For example, QCSH is used to calculate the binding energies of 12 light nuclei (i.e., 2H, 3H, 3He, 4He, 6Li, 7Li, 12C, 14N, 16O, 17O, 23Na, and 40Ca). Moreover, we experimentally demonstrate the calculation of deuteron binding energy using a superconducting quantum processor. The result indicates that QCSH can provide meaningful results already in near-term quantum devices.

Study on the mesh size determination method of blast wave numerical simulation with strong applicability

With the rapid development of computer hardware and software technology, numerical simulations have become one of the most important tools for studying propagation law of blast wave. Results of numerical simulations of explosion events greatly depend on the mesh size. The mesh size determination methods in the literature are relatively weak in generality. In this paper, a mesh size determination method with strong applicability is proposed. According to this method, the mesh size is the product of the scale coefficient and the third root of the equivalent TNT mass. The scale coefficient is related to the model dimension, scaled distance and simulation accuracy, and is independent of the TNT shape and the location of the detonation point. A large number of numerical simulation results confirm the accuracy of this method. The recommended scale coefficient to meet the engineering accuracy requirements is related to the model dimension and scaled distance. In general, when the scaled distance and model dimension are larger, the recommended scale coefficient will be larger. In this paper, the figures and tables of the recommended scale coefficients of 1D, 2D and 3D models varying with the scaled distance are given, and their rationality is verified by the existing numerical simulation events of blast wave. They can be used as a reference to determine the mesh size in numerical simulation of blast wave.

Using mobile applications for language learning as part of language classes

Over the last decade, language learning with digital technology in general, and mobile technology in particular, has received significant attention in both research and practice. This is mostly attributed to the learning-enabling aspects of mobile-assisted language learning (MALL) on the one hand, and the rapid development of hardware (mobile devices) and software (mobile applications) on the other. MALL offers the opportunity to use mobile devices for learning not only within the confines of the language classroom, but also outside it, making it particularly relevant for the implementation of distance learning. This paper examines latest practices concerning the implementation of MALL into the teaching and learning process by reviewing relevant literature from the field, with particular focus on designated mobile applications for language learning. The literature review, which identified 17 relevant publications over the period of roughly two and a half years (between January 2020 and April 2022), codes educational interventions described therein in terms of the context of language learning and the effects of introducing mobile technology into language classes. The results of the review reveal significant diversity in mobile applications employed for language learning, pedagogical approaches that underlie educational interventions, teacher roles, and positive effects for language learners, which may prove useful to language practitioners when organising language classes that include mobile technology.

Sensor-based continuous user authentication on smartphone through machine learning

With the rapid development of hardware and software technology, many end-users have often stored their data on smartphones through several in-built sensors. Thus, the security of stored data on smartphones has become a significant concern. This has fueled the importance of entry-point authentication methods on smartphones. Many entry-point authentication methods have failed to offer security due to insider or side-channel attacks. This article introduces a sensor-based continuous user authentication approach on the smartphone through multi-modal behavioral biometrics and a machine learning model to tackle the aforementioned issues. The proposed approach captures the touch and motion-based behavioral biometrics through the touchscreen and inertial sensors of the device. Then, the proposed approach extracts several features from captured behavioral data and selects the best set of features through a filter-based feature selection technique. Further, we implement a nonlinear support vector machine with optimized hyperparameters for training and predicting the features to generate the scores. We apply score-level fusion on generated scores of several sensors to compute the final score for identification of the genuine user. In this article, we systematically evaluate the proposed approach with the most commonly used behavioral activities of the smartphone user in our daily life. The experiment results on all behavioral activities show that the proposed approach obtained the best authentication score compared to the other machine learning models, and state-of-the-art methods. Finally, we conclude our article by addressing the limitations of the proposed approach and practical research issues for future exploration.

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions.

The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

Quality‐enabled decentralized dynamic IoT platform with scalable resources integration

AbstractThe Internet of Things (IoT) are standard inter connected devices aimed at join everyday object to the internet. This ecosystem include manufacturing, agriculture, smart cities, industry, as well as healthcare. The capacity of controlling and monitoring the objects of the physical world using IoT generate numerous opportunities. However some extra cost is also added to make the device globally accessible. The aggressive growth of the IoT devices, the multifariousness of IoT network technology, and the diversity of IoT use cases generate a question mark regarding the sustainability of the IoT. The aim of the proposed work to contribute in this regard, for that a dynamic integration of IoT objects that is pre determined for (i) creating an IoT platform dynamically (ii) monitoring the current status of IoT environment (iii) measuring the quality of the overall system (iv) helping to utilize all the interconnected efficiently by adding M2M communication, is introduced. Some property set which is suitable for decentralized IoT platform is also explained. Such types of dynamic IoT platform helps in every IoT application domain including industrial IoT. With the propound research, the aim is to create a dynamic IoT platform to simplify the production of next generation. The primary contribution of the proposed paper is a concept which can help to design IoT device in a faster way, which can be called rapid hardware development approach. Efficiently used human resources, that means any one having common technical knowledge can design the device, and reduce the hardware heterogeneous architecture.

A Study of Industrial Convergence in the Context of Digital Economy Based on Scientific Computing Visualization

With the rapid development of computer hardware and software and the rapid spread of network, the amount of data in scientific computing has exploded. Big data visualization has become one of the important research contents in scientific computing. Against the background of rapid development of information technology and digital technology, the scale of digital economy is increasing day by day, and China’s digital economy has ushered in unprecedented progress, and it has become an important force in leading economic development. The development of digital economy promotes industrial upgrading and transformation and facilitates industrial integration, and many traditional industries have given rise to a series of new industries. Scientific computational visualization is a new research discipline with a wide range of applications, including energy surveying, natural sciences, finance, and business. However, as a brand-new discipline, further research is needed in areas such as the application of scientific computational visualization techniques based on the digital economy environment. In this study, we propose a scientific computing visualization approach based on a big data framework for industry convergence analysis in the context of digital economy, which consists of a cloud computing platform for industry convergence in the context of digital economy by integrating neural networks in the platform, and scientific computing visualization by built-in GPU hardware. Traditional research methods for scientific computing visualization are discussed, and algorithms and techniques are classified in terms of the types of data domains studied. Finally, the further development and research directions of scientific computing visualization in the big data environment are given in conjunction with the main theoretical and application results achieved by our group in scientific computing for the field of big data visualization. Scientific computing visualization will not only promote the development and application of cutting-edge information technology, like cloud computing, big data, cyber security, artificial intelligence, computational science, system theory, and so on, but also bring a unique and disruptive development and change to the industrial integration in the context of digital economy.

Design and evaluation of intelligent teaching system on basic movements in PE

Abstract For a long time, the situation of students’ learning in physical education (PE) was not optimistic, especially the basic movement learning after class, which lacked effective online learning tools. With the in-depth research of deep neural network and the rapid development of computer hardware, the artificial intelligence technology based on deep learning has performed well in the field of basic teaching. Therefore, in this paper, an intelligent teaching system of basic movements in PE is designed. First, the information of coordinate points is collected according to the Gaussian model, and the pose of students is estimated by OpenPose. Second, the overall architecture and functional modules of the system are designed. Finally, the deviation limbs that affect the standard of overall movements are identified by the matching algorithm, which realises the evaluation and feedback of basic movements in PE. Through this teaching system, teachers can obtain the learning situation of students’ movements, and students can adjust their movements through the feedback, which achieves the convenient interaction of PE teaching.

Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain

Benefiting from the rapid development of computer hardware and big data, deep neural networks (DNNs) have been widely applied in commercial speaker recognition systems, achieving a kind of symmetry between “machine-learning-as-a-service” providers and consumers. However, this symmetry is threatened by attackers whose goal is to illegally steal and use the service. It is necessary to protect these DNN models from symmetry breaking, i.e., intellectual property (IP) infringement, which motivated the authors to present a black-box watermarking method for IP protection of the speaker recognition model in this paper. The proposed method enables verification of the ownership of the target marked model by querying the model with a set of carefully crafted trigger audio samples, without knowing the internal details of the model. To achieve this goal, the proposed method marks the host model by training it with normal audio samples and carefully crafted trigger audio samples. The trigger audio samples are constructed by adding a trigger signal in the frequency domain of normal audio samples, which enables the trigger audio samples to not only resist against malicious attack but also avoid introducing noticeable distortion. In order to not impair the performance of the speaker recognition model on its original task, a new label is assigned to all the trigger audio samples. The experimental results show that the proposed black-box DNN watermarking method can not only reliably protect the intellectual property of the speaker recognition model but also maintain the performance of the speaker recognition model on its original task, which verifies the superiority and maintains the symmetry between “machine-learning-as-a-service” providers and consumers.

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects.

Artificial chicken swarm algorithm for multi-objective optimization with deep learning

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems.

Progresses and challenges of high-order-moment turbulence closure

High-order-moment model is one of the focuses and frontier topics in the research of turbulence closure theory. Since Mr. Chou first established the Reynolds-stress transport equation in general turbulence, scholars have never stopped to study the high-order-moment model in order to predict the complex flow more accurately. Especially in the new century, thanks to the rapid development of computer hardware and the breakthrough of high-order numerical methods, turbulence simulation is in the course of turning from RANS into LES. In either RANS framework, LES framework, or the hybrid one, high-order-moment models are representative of advanced closure models. According to this, the paper gives a general review of the high-order-moment models. The focus includes the modeling approach of each source item, the evolution process of scale providing equations, and the research demand of numerical solution technology. Through several typical turbulence problems, their advantages over the traditional eddy-viscosity models are shown. Moreover, the integration of high-order-moment models in some CFD software is given. Finally, the future challenges and development direction of the high-order-moment turbulence model are prospected.

Research on Fault Detection Method of Infrared Thermal Imaging for Power Equipment based on Deep Learning

With the emergence of large image sets and the rapid development of computer hardware, especially GPU, it is a challenging problem to deploy convolution neural network (CNN) model on embedded devices with limited computing resources. The overheating fault of power equipment can be identified by collected infrared thermal imaging. Due to the propagation and fading of infrared radiation in the air, the infrared temperature measurement result is lower than the actual temperature value. In this paper, an efficient convolution neural network based on embedded equipment is proposed for thermal fault detection of power equipment. The backbone network in SSD algorithm is replaced by MobileNet, and Batch Normalization is merged with the previous convolution layer, so as to reduce model parameters, improve reasoning speed and make it run on lightweight computing platform. In order to solve the problem of propagation loss of infrared radiation in the air, an infrared temperature measurement correction unit based on BP neural network is proposed. Based on the above innovation, a thermal fault detection system for power equipment is designed. experiments and field applications show that the method has high accuracy and reasoning speed.

MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of $\ell _{2,1} $ norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non- uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.

A Pipeline Leak Detection and Localization Approach Based on Ensemble TL1DCNN

There is an increasing need for timely pipeline leak detection and localization methods, pipeline leak could lead to not only the loss of the goods but also considerable environmental and economic problems. With the rapid development of hardware and software, the pipeline leak detection and localization algorithms have been widely researched and applied in many Fields. However, traditional methods are usually limited by extracting features manually, which is inefficient and time-consuming. Convolutional neuron network is an effective method to extract features automatically. In this paper, a novel ensemble transfer learning one-dimension convolutional neural network (TL1DCNN) for the pipeline leak detection and localization is proposed. The TL1DCNN plays the role of base learner. The results of a set of obtained base learners are integrated to achieve the task of pipeline leak detection and localization. Firstly, one-dimension convolutional neural network (1DCNN) models with different parameters are pretrained with source domain data. A small learning rate is set to retrain the above 1DCNN models for target task with target domain data in order to obtain TL1DCNN base learners. Then, the four ensemble strategies with different number base learners whose ensemble weights are optimized by particle swarm optimization algorithm are obtained by minimizing the sum of similarity. The dataset simulated by pipeline network model is used to evaluate the effectiveness of the proposed approach. The indicators such as classification accuracy, precision, recall, F_score and confusion matrix are used to compare the proposed approach with traditional methods and other deep learning methods. The experimental results show that the performance of the proposed approach is superior to other compared methods.

DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

Cloud computing is a computing method based on the Internet designed to share resources through virtualization technology. For a large number of requests waiting to be processed, task scheduling is used to reasonably allocate computing resources to requests. With the rapid development of computer hardware and software, deep reinforcement learning (DRL) provides a new direction for better solving task scheduling problems. In this paper, we propose a novel DRL-based dynamic load balancing task scheduling algorithm under service-level agreement (SLA) constraints to reduce the load imbalance of virtual machines (VMs) and task rejection rate. First, we use the DRL method to select a suitable VM for the task and then determine whether to execute the task on the selected VM violates the SLA. If the SLA is violated, the task is refused and feedback a negative reward for DRL training; otherwise, the task is received and executed, and feedback a reward according to the balance of the VMs load after the task is executed. Compared with three other task scheduling algorithms applied to randomly generated benchmark and Google real user workload trace benchmark, the proposed algorithm exhibits the best performance in balancing VMs load and reducing the task rejection rate, improving the overall level of cloud computing services.