A task allocation optimization model in mechanical product development was proposed. First, Bayesian network was introduced and used to describe quality characteristic (QC) relations of mechanical products. The quality characteristic importance was then analyzed based on the Bayesian network. Weights were assigned to QCs according to the QCs importance. QCs are generally manufactured by different types

The Controller model is a heterogeneous parallel programming model implemented as a library. It transparently manages the coordination, communication and kernel launching details on different heterogeneous computing devices. It exploits native or vendor specific programming models and compilers, such as OpenMP, CUDA or OpenCL, thus enabling the potential performance obtained by using them. This work

Wireless sensor networks (WSNs) are the key technological building block of Internet of Things (IoT), by which remote users can access the real-time data from the sensor nodes. Due to the openness and mobility of such network, it is essential to establish secure links for end-to-end communication with proper authentication. Although amount of research works in this area have been made, so far designing

As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind

The intensive mobile data traffic poses a great challenge for energy-constrained mobile devices. In the mobile edge environment, effective computing offloading and resource allocation can improve the service performance of edge computing systems. Therefore, a dynamic computation offloading model based on genetic algorithm is proposed in this paper. In this strategy, a task weight cost model based on

The purposes are to explore the video image stitching technique of Unmanned Aerial Vehicles (UAVs), expand the application of image registration algorithms and new sensing equipment in video image stitching, and improve the development of video remote sensing image processing. First, how to remotely stitch UAV video images is analyzed. Second, the Scale Invariant Feature Transform (SIFT) algorithm

Intelligent cloud computing of logistics industry, especially in trunk logistics, is becoming hugely important in global economy. However, logistics information systems are heterogeneous, and each system processes lots of data; furthermore, data confidentiality in the operation process for respective stakeholders blocks information sharing, especially in the settings in China that the small and medium

A correction to this paper has been published: https://doi.org/10.1007/s11227-021-03837-x

Recently, the synthesis of reversible sequential circuits has attracted researchers’ attention for implementing low-power logic designs. So far, the direct and replacement techniques have been used in the reversible sequential circuits design. The replacement technique leads to high quantum cost. There is the principle to look for efficient reversible circuits composed of Clifford + T gates by optimizing

Internet of things (IoT) is renowned for being a massive revolution led by business leaders and researchers. Routing protocol for low-power and lossy network (RPL) is a standardized protocol that serves the routing need of the IPv6-based low-power and lossy networks, which are the significant enablers of the IoT technology. Despite its many outstanding features, RPL offers very low protection against

The Internet of things (IoT) is emerging as a prime area of research in the modern era. The significance of IoT in the daily life is increasing due to the increase in objects or things connected to the internet. In this paper, routing protocol for low power and lossy networks (RPL) is examined on the Contiki operating system. This paper used RPL attack framework to simulate three RPL attacks, namely

Due to high and unpredictable connection delays, privacy gaps, and traffic load of networks connecting cloud computing to end users in many of the Internet of Things (IoT)-based services, some challenges have been created in cloud computing efficiency. Hence, fog computing has been proposed as a solution in order to bring the cloud service closer to the existing things in the ecosystem. Integrating

High performance LINPACK (HPL) benchmark is used to evaluate the maximum floating-point performance of a computer cluster. Since the performance of the graphics processing unit (GPU) has been improved rapidly, many researchers start to optimize HPL benchmark through GPU to maximize system utilization. Nevertheless, it is difficult to determine the optimal combination of parameters in this process due

Phase noise performance and current consumption of radio frequency (RF) voltage controlled oscillator (VCO) are largely dependent on the Quality (Q) factor of inductor–capacitor (LC) tank. Because the Q-factor of the LC tank is determined by an on-chip differential inductor, we designed, analyzed, and modeled the differential inductor using the high-frequency structure simulator software (HFSS), we

Scientific workflow applications have a large amount of tasks and data sets to be processed in a systematic manner. These applications benefit from cloud computing platform that offer access to virtually limitless resources provisioned elastically and on demand. Running data-intensive scientific workflow on geographically distributed data centres faces massive amount of data transfer. That affects

In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion labels for each video sample. Based on the video data, we first choose the most significant face regions with the use of a face detection and selection step. Subsequently, we employ three CNN-based architectures to extract the high-level

In this paper, the optimum coordination of over current relay in distribution system has been performed. The time dial setting (TDS) of over current relay is optimized by minimizing total operating time of relays. As the power system is a complex and highly interconnected, the complexity may be occurred in calculating optimum relay coordination time (TDS, phase setting etc.) by conventional or analytical

The 3D ultrasound features of patients with pelvic organ prolapses (POP) were analyzed using deep learning algorithms to explore the diagnostic efficacy of deep learning in identifying the types of POP and evaluate the feasibility of pelvic floor 3D ultrasound in diagnosing POP. Forty-four POP patients were included as the research objects, including 17 cases of anterior cavity prolapse, 23 cases of

The demand of cloud-based services is growing rapidly due to the high scalability and cost-effective nature of cloud infrastructure. As a result, the size of the data center is increasing drastically, so is the cost of maintenance in terms of resource management and energy consumption. Hence, it is important to develop a proper resource management plan to maximize the profit by reducing the overhead

Flying ad hoc networks (FANETs) consist of unmanned aerial vehicles (UAVs) with energy limitations which have the capability of sending recorded live video stream to supervise their surroundings completely and intelligently. Although significant efforts have been made by previous researchers to increase the quality of received video stream as a main mission of a UAV, challenges like energy consumption

Fog computing not only executes applications in the vicinity of the IoT devices/users but also keeps transient data which removes the need for data transfer to the cloud on regular basis. For applications’ placement, an orchestrator considers the requirement of the application and the current fog status to place the application on suitable fog nodes. The orchestrator itself may be placed on fog or

Recently, the size of the used car market has been continuously increasing. In this used car trading market, various used car trading damage is caused by the asymmetry of information between the seller and buyer. In particular, there are many cases of damage caused by information asymmetry between the seller and buyer, and the reliability of the used car trading market is being deteriorated. In this

In the world, cardiovascular disease is a common, debilitating disease that affects human lives in many different ways. It is important to detect heart disease effectively and reliably in order to avoid potential heart failures. Since heart failure is a major risk, it should be successfully treated in the early stages of heart disease. Many programs exist that can diagnose heart disease at an early

Accurate and reliable smoke detection is important to prevent catastrophic fire incidents. It becomes more challenging when the smoke detection is required for wide closed area such as tunnel and subway where long distance sensing is required. Recent visible laser technology suggests that the far-infrared transmitters and receivers can be used in object detection and Zigbee wireless communication technology

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational

A class imbalance problem occurs when a dataset is decomposed into one majority class and one minority class. This problem is critical in the machine learning domains because it induces bias in training machine learning models. One popular method to solve this problem is using a sampling technique to balance the class distribution by either under-sampling the majority class or over-sampling the minority

In recent years, low back pain rehabilitation exercises have been widely performed for spine-related illnesses. To facilitate rehabilitation exercises, pose-based human action recognition technique is used to determine human movement from simple videos. Herein, we propose a new stretching side pose classification system using three-layer long short-term memory (LSTM) that can be used in rehabilitation

The aim of this exploration is to analyze the elements related to infections as well as neurological damage complications after transcatheter arterial chemoembolization (TACE) by computer information health analysis using deep learning. In this exploration, there were 80 primary liver cancer patients who were selected as the study subjects. After each patient was treated with TACE, analysis is made

Rise of Big Data techniques has led to the requirement for low latency analysis of high-velocity continuous data streams in real time. Several solutions, including Stream Processing Systems (SPSs), have been developed to enable real-time distributed stream processing. However, emerging application scenarios such as smart cities and wearable assistance that involve highly variable data rates keep on

The immense growth in the cyber world has given birth to various types of cybercrimes in the Internet of things (IoT). Cybercrimes have breached the multiple levels of cybersecurity that is one of the major issues in the IoT networks. Due to the rise in IoT applications, both devices and services are prone to security attacks and intrusions. The intrusion breaches the data packet extracted from different

Due to the problem of high link load of edge cache and small storage space of edge server, a caching architecture by the collaborative of edge nodes and the cloud server is proposed. The content cache location is designed and optimized, which can be the content provider, cloud server (CS), and edge node (EN). In the proposed system, cloud servers collaborate with edge servers and the performance of

Scheduling in multiprocessor computing systems is experiencing prolific challenges in datacenters due to the alarmingly growing need for dynamic on-demand resource provisioning. This problem has become a challenge for the cloud broker due to the involvement of the numerous conflicting performance metrics such as minimization of makespan, energy consumption and load balancing, and maximization of resource

Complex expressions are the basis of data analytics. To process complex expressions on big data efficiently, we developed a novel optimization strategy for parallel computation platforms such as Hadoop and Spark. We attempted to minimize the rounds of data repartition to achieve high performance. Aiming at this goal, we modeled the expression as a graph and developed a simplification algorithm for

This paper proposes a parallel computing analysis model HPM and analyzes the parallel architecture of CPU–GPU based on this model. On this basis, we study the parallel optimization of the ray-tracing algorithm on the CPU–GPU parallel architecture and give full play to the parallelism between nodes, the parallelism of the multi-core CPU inside the node, and the parallelism of the GPU, which improve

Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This

Cardiovascular disease (CVD) is a fatal disease that causes increased death in developing and developed nations. Among the various reasons, the increase in carotid intima media thickness (CIMT) is also a significant reason for CVD. It is expected to increase the death rate due to CVD up to 24.2 million by 2030. In previous studies, CIMT alone has been considered to identify the risk of CVD. In the

Memory bandwidth and latency constitutes a major performance bottleneck for many data-intensive applications. While high-locality access patterns take advantage of the deep cache hierarchies available in modern processors, unpredictable low-locality patterns cause a significant part of the execution time to be wasted waiting for data. An example of those memory bound applications is the exact matching

Due to problems during production the author information was incorrectly published.

Blockchain and cloud computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main mechanism to test, develop, and deliver new applications and services in a distributed manner across the World Wide Web. Large data centers host many services and store

Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also complement and extend experimental results. DNS of high Reynolds numbers, however, require huge computing cost so high-performance computing has been strongly pursued. In this study, we examine the feasibility of cost-efficient DNS on Intel

Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification

Vehicular networks are considered as one of the most pertinent research topics in intelligent transportation systems and anchors for future smart city environment due to the ability to provide road safety and precautionary measures for the drivers and passengers. Due to the characteristics of vehicular networks, security, privacy, and trust management are challenging issues. Blockchain is an emerging

Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral science. Once these sentiments are discovered, this feedback can help video creators improve the video quality and greatly enhance online users’ watching experience. Predicting these entity-level sentiments is challenging because there

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these

Phase unwrapping is an integral part of multiple algorithms with diverse applications. Detailed phase unwrapping is also necessary for achieving high-accuracy metric sensing using laser feedback-based self-mixing interferometry (SMI). Among SMI specific phase unwrapping approaches, a technique called Improved Phase Unwrapping Method (IPUM) provides the highest accuracy. However, due to its complex

The implementation of a wireless sensor network has been utilized in many applications of urban and rural areas. One such application is smart-grid Internet of Things, (IoT)-based monitoring systems that work on real-time communication. They require an intelligent scheduling mechanism to scrap delay in dynamic traffic flows without undergoing any efficiency loss. This study presents a data-driven TDMA-based

As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease

Multicore systems are in demand due to their high performance thus making application mapping an important research area in this field. Breaking an application into multiple parallel tasks efficiently and task-core assignment decisions can drastically influence system performance. This has created an urgency to find potent mapping techniques which can handle the complexity of these systems. Task assignment

Numerical simulation of multi-physical processes requires a lot of processor time, especially when solving ill-conditional linear systems arising in fluid dynamics problems. This paper is devoted to the development of efficient parallel methods for such systems for FireStar3D wildfire modeling code. Two alternative approaches are discussed and analyzed, based on the MILU-preconditioned conjugate gradient

This work aimed to explore chest computed tomography (CT) image segmentation of patients with large-cell neuroendocrine carcinoma (LCNEC) based on deep learning, as well as the clinical manifestations and imaging and pathological features of LCNEC patients. Clinical data of 40 patients with LCNEC confirmed by pathological examination in the X Hospital from December 2015 to December 2017 were retrospectively

Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal

Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system.

In this paper, a theoretical framework for Optical Wireless Communication using RGB color model with computer monitor display and digital camera is proposed. The motivation is to find a cheaper alternative to physical network switches and wired and optical cables in communication networks that can be leveraged for computer clusters, thus reducing time and costs for purchase, setup, maintenance, power

LTE networks consist of tracking areas (TAs) or a group of cells, while several TAs constitute a TA list (TAL). The LTE network adopts TAL-based registration, where, if the user equipment (UE) enters a TA that is not in its current TAL, the UE registers the TA to inform the network of its new location. A central policy for TAL allocation, known as a TA-based central policy, was proposed for TAL-based

Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this high-dimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean

The objective is to demonstrate that a probabilistic cellular automata rule can place reliably a maximal number of dominoes in different active area shapes, exemplarily evaluated for the square and diamond. The basic rule forms domino patterns, but the number of dominoes is not necessarily maximal and the patterns are not always stable. It works with templates derived from domino tiles. The first proposed