It is of great significance and practical application value to extract land-cover type accurately. However, the input data usually used in classification such as reflectance data or vegetation index are very simple and quantitative remote sensing products are rarely used. In this paper, a multi-spectral land-cover classification method based on deep learning is proposed. Using the excellent detail

Quantum computing is a reality that presents challenges to computer engineering students and practitioners. It has been claimed that it is possible to effectively teach quantum computing to undergraduate students without a physics background by means of computer programming. With this statement in mind, this study starts with a comparison and tests of different quantum programming environments: Qiskit

Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications. Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management. Resource management is a major problem especially for scientific workflows due to their heavy calculations and

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in

The strength of many security protocols lies on the computational intractability of the integer factorization and discrete logarithm problems. Currently, the best-known techniques employed are number field sieve (NFS) family of algorithms. They come under the class of sub-exponential time algorithms. This class of algorithms comprises of multiple steps. The relation collection (sieving step) is one

Nowadays internet-based applications collect and distribute large datasets, which are mostly modeled by pertinent massive graphs. One solution to process such massive graphs is summarization. There are two kinds of graphs, stationary and stream. There are several algorithms to summarize stationary graphs; however, no comprehensive method has been devised to summarize stream graphs. This is because

The rapid increase in the number of cores on chips forced the designers to invent new communication methods such as Network-on-Chip (NoC) paradigm. Advances in integrated circuit fabrications even allowed three-dimensional NoC (3D-NoC) implementations. 3D-NoCs have more advantages than their 2D counterparts such as lower area, higher throughput, better performance, and less energy consumption. However

As the infrastructure of high-performance computing, the data center network plays an important role. As network failures occur frequently, data center networks demand highly performed, robust, and energy-efficient failure recovery mechanisms. Despite process, the existing work still has a huge scope to improve to satisfy these requirements. The backup-based failure recovery schemes reserve backup

The paper describes a heuristic algorithm for solving a generalized Hermitian eigenvalue problem fast. The algorithm searches a subspace for an approximate solution of the problem. If the approximate solution is unacceptable, the subspace is expanded to a larger one, and then, in the expanded subspace a possibly better approximated solution is computed. The algorithm iterates these two steps alternately

Interest in real-time systems has grown considerably over recent years, primarily due to significant increase in the use of smart technologies and latency-sensitive applications such as cloud gaming, audio/video streaming, and smart homes. Significant work has been done on resource mapping in the cloud environment, and a number of promising results have been established accordingly where the focus

The rise of high-resolution and high-throughput sequencing technologies has driven the emergence of such new fields of application as precision medicine. However, this has also led to an increase in the storage and processing requirements for the bioinformatics tools, which can only be provided by high-performance and massive data processing infrastructures. Such technologies allow the development

Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cache-aware job scheduling. Firstly, jobs are categorized

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which

The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and processed data, data management, and analytics. Evaluating Data Quality Model Based-In-Use has gained more ground since business value could be only estimated in its used context. Despite the numerous data quality models used for regular data

Cloud-based scientific workflow systems can play an important role in the development of cost-effective bioinformatics analysis applications. There are differences in the cost control and performance of many kinds of servers in heterogeneous cloud data centers for bioinformatics workflows running, which can lead to imbalance between operational/maintenance management costs and quality of service of

With the proliferation of technology, a system of connected and interconnected devices, henceforth referred to as Internet of Things, is emerging as a viable method for automated interactions between users and environment in day-to-day life. However, such proliferation leads to an impractical task with respect to interactions among humans and devices. The major reason behind this impractical task is

Cognitive agents are typically utilized in autonomous systems for automated decision making. With the widespread use of autonomous systems in complex environments, the need for real-time cognitive agents is essential. Cognitive agents are more capable when they are able to process larger amounts of information to make more informed and intelligent decisions. The solution search space for cognitive

Cloud computing represents the latest technology that has revolutionized the world of business. It is a promising solution giving companies the possibility of remotely storing their data and accessing services whenever they are needed and at a lower cost. However, outsourcing IT resources also brings risks, especially for sensitive information in terms of security and privacy, since all data and resources

Quantum dot cellular automata (QCA)-based demultiplexer or DeMUX is a basic module of nanocommunication and nanocomputation, like a multiplexer. However, the design and analysis of demultiplexer using QCA have been neglected by researchers, unlike multiplexer. This article proposed and analyzed a simple and optimized QCA-based single-layered demultiplexer only using two majority gates, one inverter

The high performance, high efficiency, and low cost of Commercial Off-The-Shelf (COTS) devices make them attractive for applications with strict reliability constraints. Today, COTS devices are adopted in HPC and safety-critical applications such as autonomous driving. Unfortunately, the cheap natural boron widely used in COTS chip manufacturing process makes them highly susceptible to thermal (low

Cloud computing is the fastest emerging technology that proposes several resources under various pricing strategies that are specified based on temporal constraints. The main aim of cloud computing is to enhance the performance level and minimize operating costs. Thus, organizations looking towards optimizing their spending on IT infrastructure find such pricing strategies very attractive, especially

Continuous scaling-up of high-performance computing systems has brought challenges to the debugging and tuning of large-scale parallel programs. Firstly, to locate bugs in a program or tune its performance, programmer often needs to execute the program in a specified scale repeatedly, which consumes massive resources; secondly, due to the extensively used job scheduling systems, programmers can only

Quantum-dot cellular automata (QCAs) are one of the most significant state-of-the-art technologies that have exhibited the potential to replace the complementary metal oxide semiconductor. QCA offers a variety of benefits over its conventional counterpart, including size, latency, and energy consumption. Meanwhile, multiplexers are crucial to the design of arithmetic and logic circuits, and NOT-AND

Cloud computing is attractive to business owners and allows enterprises to start from the small and increase resources only when there is a rise in service demand, but cloud may become expensive. Fog computing has many advantages, and it is suited for the applications whereby real time is very important, but fog resources may also be highly limited. The cloud and fog computing may perform tasks together

Reliability analysis is one of the crucial issues for any scalable optical interconnection network. Torus is a highly scalable optical interconnect for data centre networks. The traditional torus network has XY routing algorithm. We have proposed a novel optimised routing algorithm. This paper focuses on the time-dependent and time-independent analysis for both terminal and broadcast reliabilities

A many-core parallel approach of the multilevel fast multipole algorithm (MLFMA) based on the Athread parallel programming model is presented on the homegrown many-core SW26010 CPU of China. In the proposed many-core implementation of MLFMA, the data access efficiency is improved by using data structures based on the structure of array. The adaptive workload distribution strategies are adopted on different

In scientific computing and cryptography, there are many applications that involve large integer multiplication, which is a time-consuming operation. To reduce the computational complexity, number theoretic transform is widely used, wherein the multiplication can be performed in the frequency domain with reduced complexity. However, the speed performance of large integer multiplication is still not

The introduction of elastic optical networks (EONs) leads to new challenges, such as the problem of routing, modulation level, and spectrum assignment. In the literature, the best among the shortest routes (BSR) algorithm was proposed as fixed and fixed-alternate routing for EONs, but without considering physical impairments. Therefore, call requests were handled not taking into account the different

In order to study the problems of inadequate maintenance measures, inappropriate maintenance time, and unreasonable use of funds in asphalt pavement maintenance of Highway in China, the maintenance of highway pavement is taken as the research object in this study, and a prediction model is established for preventive maintenance performance of highway by using neural network. Firstly, the performance

Balancing energy–performance trade-offs for smartphone processor operations is undergoing intense research considering the challenges with the evolving technology of mobile computing. However, to guarantee energy-efficient processor operation, layout, and architecture, it is necessary to identify and integrate optimization techniques and parameters influencing energy–performance trade-off in various

Data science applications have become widespread as a means to extract knowledge from large datasets. Such applications are often characterized by highly heterogeneous and irregular data access patterns, thus often being referred to as big data applications. Such characteristics make the application execution quite challenging for existing software and hardware infrastructures to meet their resource

Collection of web behavior relies on self-report and web logs in daily life. The self-report is derived on memory and can seem ambiguous for analyzing web behavior. It is necessary to quantitatively measure these web behaviors to see the changes over time. Individuals tied by the same emotion become a group, and its members synchronize with each other. Therefore, this study is to propose a web behavior

One of the most challenging issues in the big data research area is the inability to process a large volume of information in a reasonable time. Hadoop and Spark are two frameworks for distributed data processing. Hadoop is a very popular and general platform for big data processing. Because of the in-memory programming model, Spark as an open-source framework is suitable for processing iterative algorithms

Dr. Feng Wang’s second affiliation is missing in the original article. Both affiliations are given here.

Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem; thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. Dragonfly

Graph eigenvalues play a fundamental role in controlling structural properties which are critical considerations in the design of supercomputing interconnection networks, such as bisection bandwidth, diameter, and fault tolerance. This motivates considering graphs with optimal spectral expansion, called Ramanujan graphs, as potential candidates for interconnection networks. In this work, we explore

Augmented reality surgery has not been successfully implemented in dental implant surgery due to the negative impact of an incorrect implant placement. This research aimed to improve the convergence between computed tomography derived teeth model and real-time stereo view of patient’s teeth to provide high registration accuracy. Enhanced iterative closest point algorithm is proposed to reduce the error

Internet of things (IoT) has become a new era of communication technology for performing information exchange. With the immense increment of usage of smart devices, IoT services become more accessible. To perform secure transmission of data between IoT network and remote user, mutual authentication, and session key negotiation play a key role. In this research, we have proposed an ECC-based three-factor

Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to the researchers such as resource awareness, dynamic configurations, heterogeneous clusters, and load balancing. All of these aspects play a major role in the job scheduling process. Inefficiency in any of them causes problems for achieving the maximum throughput. SPEs must contemplate other aspects like resource

The new era of the Internet of Things is driving the evolution of conventional vehicle ad hoc networks into the Internet of vehicles (IoV). Radio frequency identification (RFID) is a reliable and advanced instrument used for automated data collection, processing and tracking, which is widely used in IoV. One of the important features of an RFID system is its ability to search for a particular tag among

Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity

By increasing the internet of things (IoT) applications, multi-channel wireless sensor networks (WSNs) are widely used as a promising technology. Due to power constraints in WSNs, increasing the network’s lifetime is a challenging issue that must be satisfied in any design, obviously medium-access control (MAC) and routing protocols. In this paper, we proposed a joint MAC and routing mechanism that

For a connected graph \(G=(V,E)\), two spanning trees \(T_1\) and \(T_2\) of G are said to be a pair of completely independent spanning trees (or a dual-CIST for short) if for any two vertices \(u,v\in V\), the paths joining u and v in the two trees have no common vertex except for u and v. Although the existence of a dual-CIST in the underlying graph of a network has the practical application of protection

Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas

Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care is the high energy consumption when sending information of sensors to the sink. Choosing the proper route when sending information to the sink will reduce energy consumption and increase network lifetime. The current paper used the shuffled

Given a set of n segments and a query shape Q, the windowing length query asks for finding the sum of the lengths of the parts of the segments that lie inside Q. The popular places problem of a set of curves asks for the subset of the plane where each query shape centered at a point of that region intersects with at least f distinct curves. For square queries, an optimal \(O(n^2)\) time algorithm and

Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise with the help of scheduling algorithms to their respective processors. For task assignment, every scheduling algorithm follows different protocols like upper bound of CPU utilization, assigning priorities, etc. In this paper, author has

The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming “Smart” thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded

Several wireless sensor networks (WSNs) simulations run in parallel computer architectures to improve their scalability. The main problem with this strategy is guaranteeing the reproducibility transparently to simulation users. We present a reproducible model for WSNs parallel simulations. The model uses a chunk partition strategy, in which all simulation elements are wrapped into chunks and simulated

A quantum-inspired hybrid scheduling technique is proposed for multi-processor computing systems. The proposed algorithm is a hybridization of principles of quantum mechanics (QM) and a nature-inspired intelligence, gravitational search algorithm (GSA). The principles of QM such as quantum bit, superposition and rotation gate help to design an efficient agent representation as well as intense exploration

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud

With the wide deployment of cloud computing in scientific computing, cost minimization is increasingly critical for large-scale scientific workflow. Unfortunately, due to the highly intricate directed acyclic graph (DAG)-based workflow and the flexible usage of virtual machines (VMs) in cloud platform, the existing workflow scheduling approaches are inefficient to strike a balance between the parallelism

In a distributed system, multiple nodes work together to build a highly available, reliable, resource shareable, and fault-tolerant system to achieve a common goal. Here, multiple nodes work together to complete a task, so coordination is essential among these nodes. Electing a node as a system leader from among all the nodes can be a possible solution to do the coordination. Besides coordination,

This paper presents a parallel low-latency multichannel source separation system designed to recover the original signals of the instruments that compound a multichannel music recording. Our approach is suitable for many applications based on interactive live broadcast classical music, where a latency of a few seconds can be assumed by online users. The obtained results show that it is possible to

SRAM-based FPGAs feature high performance and flexibility. Thus, they have found many applications in modern high-performance computing (HPC) systems. These systems suffer from the limitation of the computing resources problem for running HPC applications. Therefore, multi-FPGA systems have been emerged to alleviate such resource limitations. In this regard, efficient scheduling strategies are required

A learning automaton (LA) can be considered as an abstract system with a finite set of actions. LA operates by choosing an action from the set of its actions and applying it to the stochastic environment. The environment evaluates the chosen action, and automaton uses the response of the environment to update its decision-making method for selecting the next action. This process is repeated until the

Wi-Fi indoor positioning systems are based on received signal strength indicator (RSSI) measurements and fingerprinting, by matching measured data with the database. Hence, generation of RSSI fingerprint database is essential for a Wi-Fi-based indoor positioning system; this requires significant time and effort. The study utilizes virtual access points to increase the number of access points in an

Considering a software defined network, distributed controller architecture uses multiple controllers in which each controller manages a part of the network. The load imbalance problem in this architecture causes a large number of switch migrations resulting in a significant increase in switch migration cost and average network response time along with a decrease in throughput. Although recent studies