Development Of Hardware Technology Research Articles

Artificial intelligence changing the future of healthcare diagnostics

Artificial intelligence (AI) is a computer’s capacity to carry out operations like speech and image recognition and decision-making that ordinarily require human intelligence. Healthcare is using AI to automate tasks such as medical image analysis and diagnosis that require high precision and accuracy. The healthcare industry is significantly impacted by the rapid development of machine learning algorithms, which are frequently implemented using deep learning, as well as the growth of digital data and computing power supported by improvements in hardware technologies. Significant progress has been made in the field of artificial intelligence in recent years and is now widely used in healthcare to automate a variety of tasks, which require a high degree of accuracy and precision. The creation of machine learning algorithms, which can learn from data and make predictions based on that learning, has made it possible to use AI in healthcare. Neural networks are used in deep learning, a subfield of machine learning, to simulate how the human brain functions. Crucial advances have been made in clinical decision support, drug discovery, and medical imaging. Furthermore, the rapid development of hardware technologies, such as graphics processing units, has allowed AI systems to process enormous amounts of data quickly and accurately. Due to this, AI-based tools and platforms can help healthcare professionals with tasks such as patient monitoring, disease diagnosis, and treatment planning.

A rapid 3D magnetotelluric forward approach for arbitrary anisotropic conductivities in the Fourier domain

Previous studies have shown that anisotropy generally exists in geological bodies such as sedimentary rocks and fault zones, and more and more attention has been paid to the arbitrary conductivity media in surveys with the magnetotelluric sounding method. With the breakthrough development of computer hardware technology, large-scale 3D magnetotelluric modeling in anisotropic media has gradually become possible. At present, there are 3D magnetotelluric field simulation algorithms based on finite differences or finite elements for arbitrary anisotropic conductivity. In order to solve the common computational efficiency problems of the existing algorithms, we proposed a rapid 3D magnetotelluric forward approach for arbitrary anisotropic conductivity in the Fourier domain. Through the 2D Fourier transform, the governing equation can be converted from the space domain to the Fourier domain, thereby greatly reducing the calculation amount of the numerical simulation and improving the calculation efficiency. Then, the classical 1D anisotropy model is used to verify the correctness and the computational efficiency. Finally, the 3D anisotropic models of land and ocean are calculated, and the influence characteristics of the anisotropic medium on the magnetotelluric response are analyzed. The proposed algorithm will be used in the inverse imaging technique for large-scale 3D anisotropic data in future studies.

Quantum Computing: Progress and Innovation

The field of quantum computers has reached the point of having real systems available for experimentation to a broad community. In this article, we review recent developments in hardware technology, outline challenges and innovations in quantum systems, and discuss efforts to cultivate and educate a quantum-ready community.

CRSM: Computation Reloading Driven by Spatial-Temporal Mobility in Edge-Assisted Automated Industrial Cyber-Physical Systems

Edge Computing, as an emerging computing mode, transfers computing capacity from the cloud center to the edge of new generation automation sensor networks. However, the surge in the number of sensor devices has resulted in the edge server overload. Fortunately, with the development of hardware technology, the computing capacity of sensor devices has improved. Therefore, we introduce a new concept of computation reloading, that is, resource-rich servers allocate tasks to sensor devices with stronger computing capacity. Most previous real-time EC researches have not considered sensor devices’ higher spatial–temporal mobility, which results in continuous computation migration and high delay. In this article, we expose a computation reloading scheme driven by spatial–temporal mobility (CRSM), for single time slice and multiple time slices cases, computation tasks are allocated in advance by predicting sensor devices locations. The experiments demonstrate that our scheme outperforms other methods in terms of shorting industrial cyber-physical systems delays.

A Deep Learning Model Collaborates with an Expert Radiologist to Classify Brain Tumors from MR Images

The brain, which consists of nerve cells called neurons, is the center of the nervous system. The rapid and abnormal growth of nerve cells by interacting with each other is called a brain tumor. Undiagnosed or delayed diagnosis of brain tumors lead to death. Although it depends on experience, manually diagnosing and classifying brain tumors is challenging for physicians. Artificial intelligence-based computer systems can help doctors detect brain tumors using the developments in hardware technology and the amount of data increasing daily. This study proposes a deep learning-based system to classify brain MRI images as tumorous or normal using the pre-trained EfficientNet-B0 model. Our radiologist validated a public dataset containing 3000 brain MRI images. The dataset is divided into 70% train, 20% validation, and 10% test. In the test phase after the training, the pre-trained EfficientNet-B0 model achieved high performance with 99.33% accuracy, 99.33% sensitivity, and 99.33% F1 score. In addition, in the evaluation of the test images, the heat maps obtained by the Grad-CAM method were examined by our radiology specialist. The result of evaluations shows that the pre-trained EfficientNet-B0 deep model chooses the right focus areas in its predictions and can be used for clinical tumor detection due to its explainable structure.

Research on SLAM Road Sign Observation Based on Particle Filter.

With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality.

A Comparative Assessment of Text-independent Automatic Speaker Identification Methods Using Limited Data

Automatic Speaker Identification (ASI) is one of the active fields of research in signal processing. Various machine learning algorithms have been used for this purpose. With the recent developments in hardware technologies and data accumulation, Deep Learning (DL) methods have become the new state-of-the-art approach in several classification and identification tasks. In this paper, we evaluate the performance of traditional methods such as Gaussian Mixture Model-Universal Background Model (GMM-UBM) and DL-based techniques such as Factorized Time-Delay Neural Network (FTDNN) and Convolutional Neural Networks (CNN) for text-independent closed-set automatic speaker identification on two datasets with different conditions. LibriSpeech is one of the experimental datasets, which consists of clean audio signals from audiobooks, collected from a large number of speakers. The other dataset was collected and prepared by us, which has rather limited speech data with low signal-to-noise-ratio from real-life conversations of customers with the agents in a call center. The duration of the speech signals in the query phase is an important factor affecting the performances of ASI methods. In this work, a CNN architecture is proposed for automatic speaker identification from short speech segments. The architecture design aims at capturing the temporal nature of speech signal in an optimum convolutional neural network with low number of parameters compared to the well-known CNN architectures. We show that the proposed CNN-based algorithm performs better on the large and clean dataset, whereas on the other dataset with limited amount of data, traditional method outperforms all DL approaches. The achieved top-1 accuracy by the proposed model is 99.5% on 1-second voice instances from LibriSpeech dataset.

Application of internet of things information security in the informationization of sports training and education

In recent years, the information technology of the Internet of Things has been developed by leaps and bounds, and the development of hardware technology has promoted the development of software technology. New concepts, new applications and new products emerge endlessly. Computer vision technology has made great progress, and industrial cameras are developing rapidly and are widely used in real life. The realization of high-definition capture and imaging functions of industrial cameras creates conditions for obtaining scientific data. This article aims to study the application of information security of the Internet of Things in the informationization of sports training and education. Correctly and objectively evaluate and understand the information security level of the Internet of Things. The guidance of computerization of sports training and education is based on the continuous and effective comparison of the implementation effect of computerization and the development strategy, and then the improvement of computerization work. Formulate the correct information strategy and provide assistance in specific implementation. On the basis of benefit, pragmatism and overall planning, formulate the correct information strategy and provide assistance in specific implementation. On the basis of benefit pragmatism and overall planning. Through experiments to master the Internet information security mechanism and use experimental questionnaire surveys and other methods to explore the application of Internet of Things information security in the informationization of sports training education, modern education must make full use of information technology, introduce physical education classroom teaching, and realize modern teaching. The experimental results of this article show that the application effect of Internet of Things information security information technology in physical education has been significantly improved. 60% of students have created better conditions for the current informationization of physical training and education, so that students can not only learn theoretical knowledge, but also deepen the understanding of sports in information teaching.

An analysis of deep neural network models for image recognition applications

The development of hardware technology and information technology has promoted the development of image recognition technology. Today, image recognition technology has been applied to many national defense technologies; especially target image recognition technology is widely used in the field of air threat prevention. However, nowadays, the air target recognition technology has the disadvantage of high misjudgment rate. The main reason is that the sky is too large and the distance gap makes it difficult to distinguish the target image from other noise images. This paper takes the neural network as the classification tool, through image preprocessing and contour extraction, establishes the recognition model of the target image. The simulation results of 10 data sets show that the method used in this paper is more than 85% accurate, but the error rate is only 0.7%. The simulation results show that the model designed in this paper can achieve air target recognition very well.

Lightweight Design of Aircraft Virtual Scene and Wireless Transmission

The concept of virtual reality, which is an important role in aircraft simulation training, was first proposed in the aerospace field in the 1960s. In recent years, with the development of hardware technology, virtual reality technology has been widely used in games, entertainment, medical and other civilian fields. In the field of civil aircraft, virtual reality technology mainly focuses on simulated flight, cockpit pilot training, aircraft maintenance training, etc. This article proposes a customer-oriented aircraft virtual scene design and implementation method, and describes the design process from aircraft 3D model construction, lightweight processing, virtual scene rendering, and WebRTC-based wireless video streaming. The main technical difficulties solved in this paper include two parts: one is to convert the existing aircraft 3D engineering model into a polygonal model that can be used for VR rendering, and the other is to realize the problem of multi-user online interactive access to virtual scenes. Through this system, users can do engineering evaluation and experience evaluation during the whole process of aircraft product design, development and sales. The application scenarios designed in this paper further expand the application of virtual reality technology in the field of civil aircraft development and lay the foundation for the development of more application scenarios in the future.

A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

With the development of hardware technology, we can collect increasingly reliable time series data, in which time series anomaly detection is an important task to find problems in time and avoid risks. It is not easy to establish a multivariate time series anomaly detection system, because the collected data not only have different attributes, scales, and characteristic information but also have horizontal and vertical connections among these data collected by various sensors. In addition, there is no clear boundary regarding whether the data are abnormal, and there is currently no unified definition of multidimensional time-series anomalies. Recently, deep learning methods have shown outstanding advantages in the processing of multidimensional time series. In this paper, we propose a definition of point anomalies in multivariate time series and an unsupervised deep learning method, the multilayer convolutional recurrent autoencoded anomaly detector (MCRAAD), which is used to detect anomalies in multivariate time series. We calculate the feature matrix sequence through the data in the sliding window, extract the characteristics of the feature matrix sequence by a multilayer convolutional encoder, obtain the time relations in the feature matrix by using several ConvLSTM units, and finally reconstruct the feature matrix sequence with the convolutional decoder to predict the self-feature matrix. In addition, we propose a threshold setting method to assist with the determination of anomalies. Finally, we test our model on synthetic datasets and a real dataset of house monitoring. The results of experiments show that our method is superior to these basic models in detecting ability and robustness. This model also provides an effective method for multivariate time-series anomaly detection in real life.

Motion Reveal Emotions: Identifying Emotions From Human Walk Using Chest Mounted Smartphone

Emotion recognition via gait analysis is an active and key area of research because of its significant academic and commercial potential. With recent developments in hardware technology, the use of inertial sensors allows researchers to effectively capture the human motion data for gait analysis. To this end, the aim of this paper is to identify emotions from the inertial signals of human gait recorded by means of body mounted smartphones. We extracted a manually-crafted set of features computed from the human inertial gait data which are used to train and precisely predict the human emotions. Specifically, we collected the inertial gait data of 40 volunteers by means of smartphone’s on-board inertial measurement units (3D accelerometer, 3D gyroscope) attached at the chest in six basic emotions including sad, happy, anger, surprise, disgust and fear . Using stride based segmentation, the raw signals are first decomposed into individual strides. For each stride, a set of 296 spectro-temporal features are computed, which are fed into two supervised learning predictors namely Support Vector Machines and Random Forest. The classification results obtained with the proposed methodology and validated with k-fold validation procedure show classification accuracy of 95% for binary emotions and 86% for all six categories of emotions.

Penerapan Metode Simple Additive Weighting (SAW) dalam Sistem Pengambilan Keputusan Order Persediaan Barang Berbasis Web

Nowaday, computers already can be used to provide choices in taking of the decision, not only used for data processing or presenting information, process of taking the decision it self is the results of a system that called Sistem Pendukung Keputusan (SPK). The system of taking the decision is possible because there has been a development of hardware technology, integration of hardware and software. In the business sector one of them is a truck and expedition which engages in the transportation services of stuffs between Lampung-Surabaya cities and vice versa. In this expedition service consists of arround used by 76 units of cars. And the supply of stuffs still used bookkeeping which is sometimes taking of the decision supply of stuffs is still handled by superiors.
 Therefore, this research used Simple Additive Weighting (SAW) method which is used to make decisions in problems solving by create a design system and building software. This research will create a system when taking the decision in the supply of stuffs in the Business Entity Expedition Services with SAW method by using WEB.

The transplantation technology of communication intensive applications on heterogeneous clusters

AbstractCompared with traditional clusters, heterogeneous clusters cherish the high performance to price ratio. However, compared with the rapid development of hardware technology, the current software technology is still lagging behind. This article introduces a parallel programming model to describe the multidimensional structure, arrangement, and communication modes of the data, threads, and fast Fourier transform. This model provides a new mechanism for transplantation between different types of heterogeneous systems. Using direct methods of turbulence simulation as an example, this article discusses the new software technologies of the parallel software theory, methods of program design, algorithm design, and system software support and provides experience for application transplantation on these heterogeneous systems.

Stand parameter extraction based on video point cloud data

Monitoring sample plots is important for the sustainable management of forest ecosystems. Acquiring resource data in the field is labor-intensive, time-consuming and expensive. With the rapid development of hardware technology and photogrammetry, forest researchers have turned two-dimensional images into three-dimensional point clouds to obtain resource information. This paper presents a method of sample plot analysis using two charge-coupled device (CCD) cameras based on video photography. A handheld CCD camera was used to shoot the sample plot by surrounding a central tree. Video-based point clouds were used to detect and model individual tree trunks in the sample plots and the DBH of each was estimated. The experimental results were compared with field measurement data. The results show that the relative root mean squared error (rRMSE) of the DBH estimates of individual trees was 2.1–5.7%, acceptable for practical applications in traditional forest inventories. The rRMSE of height estimates was 2.7–36.3%. Average DBH and heights, and tree density and volume were calculated. Video-based methods require compact observation instruments, involve low costs during field investigations, acquire data with high efficiency, and point cloud data can be processed automatically. Furthermore, this method can directly extract information on the relative position of trees, which is important to show distribution visually and provides a basis for researchers to regulate stand density. Additionally, video photography with its unique advantages is a technology warranting future attention for forest inventories and ecological construction.

Applicability of Various Pre-Trained Deep Convolutional Neural Networks for Pneumonia Classification based on X-Ray Images

Along with the development of machine learning methodologies, in the last few decades, a variety of machine learning techniques began to solve many problems in various fields. Encouraged by the development of computer hardware technology, deep learning has become one of machine learning methods that have become incredibly attractive due to the performance of such methods. By using deep learning, the researchers managed to find solutions to a variety of complex problems that had not yet had an optimal solution. In the field of bioinformatics and computer-aided diagnosis, previous researches show that deep learning can be utilized to predict the structure of protein compounds, detecting lumbar spinal stenosis regions, or other biomedical related prediction problems. In this study, we are experimenting with various pre-trained deep convolutional neural networks (CNN) architectures, namely, densely connected convolutional networks (DenseNet), VGG-16, Inception, and Inception-ResNet, to build another classifier on top of before-mentioned pre-trained models for classify each patient condition based on their chest X-Ray images. In this study, our resulted model needs to able to predict whether a patient’s lungs have a normal condition or have contracted either with viral or bacterial pneumonia-related conditions based on the given chest X-Ray image. Our experiment shows that by using a variation of DenseNet, DenseNet201,deep CNN is able to achieve the accuracy scores of 93.87% for the training data and 82.7% for the validation data.

A Lightweight Three-Factor User Authentication Protocol for the Information Perception of IoT

With the development of computer hardware technology and network technology, the Internet of Things as the extension and expansion of traditional computing network has played an increasingly important role in all professions and trades and has had a tremendous impact on people lifestyle. The information perception of the Internet of Things plays a key role as a link between the computer world and the real world. However, there are potential security threats in the Perceptual Layer Network applied for information perception because Perceptual Layer Network consists of a large number of sensor nodes with weak computing power, limited power supply, and open communication links. We proposed a novel lightweight authentication protocol based on password, smart card and biometric identification that achieves mutual authentication among User, GWN and sensor node. Biometric identification can increase the non-repudiation feature that increases security. After security analysis and logical proof, the proposed protocol is proven to have a higher reliability and practicality.

The Application of Simulation Technology in Medical Education

Virtual simulation technology is a method of image rendering through computer. Compared with the traditional image representation, virtual simulation technology is a big jump. With the rapid development of computer hardware technology, virtual simulation technology has developed now in all fields of life. This paper mainly introduces the composition, the working principle and the application of the virtual simulation of three-dimensional interactive platform in the field of medical education.

The Application of Database Technology in Information Society and its Existing Problems

With the development of computer technology and network communication technology and multimedia technology,database technology has become an important technical means and software technology for organizing and managinglarge amount of data in information society. It is the foundation of network information management system. Itnot only has a complete theoretical basis, and with the rapid development of hardware technology and softwaretechnology, its application is more and more widely. However, the rapid development of information technology isthat people face such a situation: CAD / CAM, CIMS, CASE, GIS and other applications require the managementof increasingly complex information, such as multimedia data, such as multimedia data, spatial data, scientifi c data,Data and other data types, these data compared with the traditional type, not only the type of complex, diverse forms,and storage, transmission, operation and retrieval methods also have many new features, new requirements, on theone hand, the need for database technology and database systems To manage these complex data in order to obtainthe database system has many of the data management functions, such as query, retrieval, recovery, concurrencycontrol, integrity, storage management. On the other hand, due to the vigorous development of network technology,information processing has long been from a single user environment to the development of the network environment,this environment for resources, information sharing provides a great convenience, and because of multi-machine worktogether to greatly improve the work , The combination of the two requires that the database management system beable to manage complex data objects in a networked environment or heterogeneous environment. Traditional DBMSscannot eff ectively handle complex multimedia data, thus requiring the use of new multimedia indexing and retrievaltechniques. Multimedia database technology is an important part of computer technology, it can simultaneously acquire,process, edit, store and display text, sound, video, graphics and other diff erent media, while it has diversity, integrationand interactivity and so on. Only to solve the key technical issues of multimedia, can be widely used to promote itsfaster development.

An error-detecting approach for fault tolerance parallel recomputing with parallel digital terrain analysis

In recent years, the research of parallel digital terrain analysis has become a hot spot. Using parallel computing technology to solve data-intensive computing problems has become a new trend in digital terrain analysis (DTA). On the other hand, with the development of hardware technology and new applications, how to ensure the reliability of the results of parallel computing is one of key problems. We can improve the system’s ability to provide the right service by properly adopting fault tolerance technology. A parallel error-detecting approach based on parallel recomputing technology is presented and implemented by the combination of redundant process and multi-threads technology. Adopting a parallel comparison between the results of the process and the ones of its copy process, on the same data block, it can improve the efficiency of fault tolerance parallel recomputing. Furthermore, considering the relationship between the error-detection of the results and the recomputing, a modified scheme is proposed to make them to be executed concurrently. According to the error-detecting analysis of slope algorithm from DTA, it proves the effectiveness of the error-detecting approach based on fault-tolerant recomputing and achieves minor overhead.