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Terahertz (THz, $0.3\!\!-\!\!3$ THz) wireless access is nowadays considered as a major enabling technology for sixth generation (6G) cellular systems. To compensate for high propagation losses, these systems will utilize antenna arrays with extremely directional beams. The performance of such systems will thus be heavily affected by micromobility such as shakes and rotations of user equipment (UE) even when user is in stationary position. The ultimate impact of micromobility is spontaneous degradation of signal-to-noise (SNR) level leading to outages. In this paper, we measure and statistically investigate the micromobility process of various applications including video viewing, phone calling, virtual reality viewing and racing game. Particularly, we characterize occupancy distributions and first-passage time (FPT) to outage for various antenna configurations. We also assess the radial symmetry in micromobility patterns and characterize distance-dependent velocity and drift to the origin parameters. The obtained results are essential for developing mathematical models of micromobility patterns that needs to be further used in system-level analysis of THz cellular systems. To this end, we also illustrate that Markov models are only suitable for applications with low and purely random micromobility dynamics such as video viewing and phone calling. When a user is controlled by the application, as in the case of gaming, Markov models overestimate FPT to outage. [ABSTRACT FROM AUTHOR]

The integration of Software-Defined Networking (SDN) technology in Smart Grids has emerged as a transformative approach to modernizing energy infrastructures and enhancing operational efficiency. This comprehensive review paper explores the advancements, challenges, and future perspectives of SDN implementation in Smart Grid environments. It delves into the applications of SDN in areas such as real-time monitoring, energy distribution optimization, grid resilience, integration of renewable energy sources, and demand response management. Additionally, the paper addresses key challenges including security concerns, interoperability issues, scalability constraints, and regulatory compliance requirements that accompany the adoption of SDN in Smart Grids. Looking ahead, the paper discusses future perspectives such as leveraging artificial intelligence, edge computing, and blockchain technology to further enhance the capabilities of SDN in Smart Grids. Through an in-depth analysis of current developments and future trends, this review provides valuable insights for researchers, practitioners, and policymakers seeking to harness the full potential of SDN for advancing Smart Grid infrastructures.

Traces the history of the first 40 years of the International Symposia on Discharges and Electrical Insulation in Vacuum. First symposia held in October 1964 at Cambridge, Massachusetts; History of the Permanent International Scientific Committee (PISC); Presentation formats; Value to participants; Awards; Developments in techniques used in papers presented at the symposia; Developments in the fields of the symposia.

Much progress has recently been made in global optimization, with particular attention devoted to robust nature-inspired stochastic methods for difficult, high-dimensional problems. This paper presents a computational study of an adaptation of one such method, particle swarm optimization (PSO), which is analyzed for parallelization on readily-available heterogeneous parallel computational hardware: specifically, multicore technologies accelerated by graphics processing units (GPUs), as well as Intel Xeon Phi co-processors accelerated with vectorization. In this heterogeneous approach, computationally-intensive, task-parallel components are performed with multicore parallelism and data-parallel elements are executed via co-processing (GPUs or vectorization). A computationally intensive adaptive PSO technique is parallelized according to this schema. In experiments with two high-dimensional and complex functions, large speedups can be obtained. Thus, a heterogeneous approach mitigates the time complexity of PSO adaptations, suggesting that other time-intensive stochastic methods can also benefit from the techniques proposed here. [ABSTRACT FROM PUBLISHER]

While the static indoor geo-location of mobile terminals (MTs) has been extensively studied in the last decade, the prediction of the trajectory of an MT is still a major problem when designing mobile location (tracking) systems (TSs). In fact, Global Positioning System (GPS) works quite well in outdoor conditions and relatively unobstructed spaces, but falls short in many urban conditions and other realistic use cases. It is important to augment mobile geo-location architectures with a prediction dimension to deal with distortions caused by obstacles, and ultimately produce a more accurate positioning system. Different prediction approaches have been proposed in the literature, the most common is based on prediction filters such as linear predictors (LPs), Kalman filters (KFs), and particle filters (PFs). In this paper, we take the prediction one step further by using digital fractional integration (DFI) to predict the actual trajectory of MTs. We evaluate the performance of our proposed DFI prediction in two indoor trajectory scenarios inspired by typical user mobility patterns in typical indoor conditions (museum visit and hospital doctor walk). To illustrate the efficiency of the proposed method in particularly noisy environments, we consider two other MT trajectory scenarios, namely spiral and sinusoidal trajectories. Experimental results show a significant performance improvement over most common predictors in the relevant literature, particularly in noisy cases. Extensive study of short-archive principle using 5, 10, and 25 previous estimated positions, showed the benefit of using DFI operator with only the most recent locations of an MT. [ABSTRACT FROM AUTHOR]

The 15th Workshop on Mobile Computing Systems and Applications (HotMobile 2014) was had over 90 attendees participating in two days of presentations and lively discussion. Learn about each of the sessions and prevalent themes throughout. [ABSTRACT FROM PUBLISHER]

Switched reluctance machines (SRMs) are witnessing increased interests and applications in the industry and scientific communities thanks to the advantages of rigid structures, high reliability and robustness, the absence of permanent magnets, fast dynamic response, and low manufacturing cost. SRMs have become a feasible and popular alternative to conventional electric machines, such as induction machines and permanent magnet machines, with variable speed drives in many applications. Since improving the electromagnetic and thermal performances of an SRM at the design stage is of significant value, this paper presents an in-depth literature review on the status and potential trends of the technology pertinent to the design and optimization of SRMs in the following aspects: the mathematical modeling of the electromagnetic and thermal fields in SRMs, the enhancement of the performances in terms of the torque ripple, acoustic noise, efficiency, and torque density, and the multiobjective design optimization incorporating all the above factors. Finally, the presence of SRMs and their design considerations for specific applications in electric vehicles, aircraft and aerospace systems, wind generators, high speed, and energy storage systems are extensively discussed in this paper. The existing approaches advancing the SRM technologies are systematically and comprehensively summarized and compared for each category. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices and the development of the wireless technologies, humans can be connected ubiquitously. Because of the mobility of the devices, it is a hard task to maintain the end-to-end path between source node and destination node. Researchers have introduced analysis of nodes’ social behavior to solve the problem of data dissemination in the networks, which leads to the emersion of the Mobile Social Networks (MSNs). They increase the performance of data forwarding by the social relationships and interactions among nodes. Many schemes and algorithms have been proposed to enhance data forwarding performance and provide humanized service by introducing social features and digging social properties. In this paper, first, we investigate the architectures and evolutionary process of the MSNs. Then, the social features of nodes existing in the MSNs and main social properties of MSNs are described. In term of the state-of-the-art works, data forwarding strategies in the MSNs are divided into four categories, including only encounter history-based strategies, social-based strategies, incentive mechanisms, and forwarding methods that take social selfishness into consideration of this paper. Finally, the major issues and challenges are discussed. [ABSTRACT FROM AUTHOR]

The Software Defined Network in Internet of Things (SDN-IoT) is enjoying growing popularity due to its flexibility, automaticity and programmability. However, there is still a lack of proper permission management on SDN-IoT applications (SIApps), especially when the SIApp’s required northbound interfaces are located in multiple heterogeneous controllers without mutual trust. Existing access control methods are usually based on centralized models, proprietary controllers, trusting conditions or manual operations. It can incur unnecessary performance degradation and poor scalability. To solve this problem, this paper proposes a SIApps’ ledger (SILedger), an open, trusted, and decentralized access control mechanism based on blockchain and attribute-based encryption (ABE). It can not only support effective authorization of SIApps in heterogeneous and untrusted SDN-IoT control domains, but also record all interactions between SIApps and resources, and thus facilitate SIApps further charging, analysis and audit. The main idea is that the SIApps are authorized using access tokens encrypted by ABE, and these tokens are seen as the currency of blockchain to be distributed. Specifically, we re-design blockchain transaction, token encryption, token initialization and token update schemes to achieve cross-domain, fine-grained and flexible SIApps’ permission management. In order to mitigate the delay and complexity problem of blockchain and ABE, we devise an access control framework that separates authorization from call process of SIApps. Finally, we perform security analysis and implement a FISCO-BCOS-based prototype for SILedger. The experimental results show that it can provide effective access control for SIApps with negligible overheads. [ABSTRACT FROM AUTHOR]

The disease-free growth of a plant is highly influential for both environment and human life. However, there are numerous plant diseases such as viruses, fungus, and micro-organisms that affect the growth and agricultural production of a plant. Grape esca, black-rot, and isariopsis are multi-symptomatic soil-borne diseases. Often, these diseases may cause leaves drop or sometimes even vanishes the plant/plant vicinity. Hence, early detection and prevention becomes necessary and must be treated on time for better grape growth and productivity. The state-of-the-art either involve classical computer vision techniques such as edge detection/segmentation or regression-based object detection applied over UAV images. In addition, the treatment is not viable until detected leaves are classified for actual disease/symptoms. This results in increased time and cost consumption. Therefore, in this paper, a grape leaf disease detection network (GLDDN) is proposed that utilizes dual attention mechanisms for feature evaluation, detection, and classification. At evaluation stage, the experimentation performed over benchmark dataset confirms that disease detection network could be fairly befitting than the existing methods since it recognizes as well as detects the infected/diseased regions. With the proposed disease detection mechanism, we achieved an overall accuracy of 99.93% accuracy for esca, black-rot and isariopsis detection. [ABSTRACT FROM AUTHOR]

Metaverse is a virtual world platform that enables users to create and explore 3D environments. Recently, this technology has gained traction due to its potential to revolutionize the way people interact and share information. This paper provides an overview of the current state of Metaverse technologies. It begins by introducing their core components and surveys recent research and development activities in the area. Next, the paper examines the potential applications of Metaverse technology in different domains, such as gaming, social networking, training, education, healthcare, and marketing. It then outlines the challenges and opportunities associated with the development and deployment of Metaverse technologies, including scalability, privacy, and security issues. Finally, the paper concludes with a discussion of the implications of Metaverse technologies for the future of digital communication and information sharing.

The purpose of this paper is to fill a gap in the existing literature about the general analysis of airport activities and detailed specifications for 5G implementation. The gap spans from overgeneralized studies to highly specific ones as will be mentioned in the paper. This research aims to bridge this gap by identifying the requirements of 5G implementation in various airport use cases, proposing detailed 5G solutions for these cases. As is well known, 5G is the Fifth Generation of cellular communications, aiming to provide high-throughput high-reliability connectivity to greatly diversified scenarios. With this objective, it shall act as a viable solution for environments as complex as an airport terminal, whose dynamic work cycle includes a wide range of diversified activities. As such, 5G capacities are assessed, identifying those airport processes that can benefit from its application as well as the general requirements of the network deployed for such scenarios. From the identified use cases, the streaming of high quality video for aircraft visual inspection as well as the monitoring of luggage trolleys via an IoT system are developed and evaluated, showing the capabilities of the approach.

ChatGPT, a groundbreaking natural language processing technology released a few months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper is a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

Large-scale image search has been attracting lots of attention from both academic and commercial fields. The conventional bag-of-visual-words (BoVW) model with inverted index is verified efficient at retrieving near-duplicate images, but it is less capable of discovering fine-grained concepts in the query and returning semantically matched search results. In this paper, we suggest that instance search should return not only near- duplicate images, but also fine-grained results, which is usually the actual intention of a user. We propose a new and interesting problem named fine-grained image search, which means that we prefer those images containing the same fine-grained concept with the query. We formulate the problem by constructing a hierarchical database and defining an evaluation method. We thereafter introduce a baseline system using fine-grained classification scores to represent and co-index images so that the semantic attributes are better incorporated in the online querying stage. Large-scale experiments reveal that promising search results are achieved with reasonable time and memory consumption. We hope this paper will be the foundation for future work on image search. We also expect more follow-up efforts along this research topic and look forward to commercial fine-grained image search engines. [ABSTRACT FROM PUBLISHER]

After the inclusion of the feature selective validation method into the IEEE Std. 1597.1/2, more and more attention has been paid to the performance evaluation and enhancements of this technique as well as broadening of applications. This paper reviews the progresses in these domains by first considering the body of work and the virtual team that is responsible for the method development, and offers conclusions about the validity, the correct implementation, and further applications of the feature selective validation technique in other domains besides the classic computational electromagnetics and electromagnetic compatibility. [ABSTRACT FROM AUTHOR]

I/O is becoming one of major performance bottlenecks in NAND-flash-based mobile devices. Novel nonvolatile memories (NVMs), such as phase change memory and spin-transfer torque random access memory, can provide fast read/write operations. In this paper, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, virtualized flash (vFlash), is also proposed to manage the unified architecture. Within vFlash, interapp and intra-app techniques are proposed to optimize the application performance by exploiting the historical locality and I/O access patterns of applications. Since vFlash is on the bottom of the I/O stack, the application features will be lost. Therefore, we also propose a cross-layer technique to transfer the application information from the application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. [ABSTRACT FROM AUTHOR]

This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years. [ABSTRACT FROM AUTHOR]

After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future. [ABSTRACT FROM AUTHOR]

Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The mixed-integer version of these problems is a new class of interest to the optimization community and practitioners, in which certain variables are required to be integers. In this paper, we describe five applications that lead to stochastic mixed-integer second-order cone programming problems. Additionally, we present solution algorithms for solving stochastic mixed-integer second-order cone programming using cuts and relaxations by combining existing algorithms for stochastic second-order cone programming with extensions of mixed-integer second-order cone programming. The applications, which are the focus of this paper, include facility location, portfolio optimization, uncapacitated inventory, battery swapping stations, and berth allocation planning. Considering the fact that mixed-integer programs are usually known to be NP-hard, bringing applications to the surface can detect tractable special cases and inspire for further algorithmic improvements in the future.

Medical activity requires responsibility not only based on knowledge and clinical skills, but also in managing a vast amount of information related to patient care. It is through the appropriate treatment of information that experts can consistently build a strong policy of welfare. The primary goal of decision support systems (DSSs) is to give information to the experts where and when it is needed. These systems provide knowledge, models, and data processing tools to help the experts make better decisions in several situations. They aim to resolve several problems in health services to help patients and their families manage their health care by providing better access to these services. This paper presents a deep review of the state of the art of smart DSSs. It also elaborates on the latest developments in intelligent systems to support decision-makers in health care. The most promising findings brought in literature are analyzed and summarized according to their taxonomy, application area, year of publication, and the approaches and technologies used. Smart systems can assist decision-makers to improve the effectiveness of their decisions using the integration of data mining techniques and model-based systems. It significantly improves the current approaches, enabling the combination of knowledge from experts and knowledge extracted from data. [ABSTRACT FROM AUTHOR]

Handle-driven image warping based on linear blending is widely used in many applications because of its merits on intuitiveness, efficiency, and ease of implementation. In this paper, we develop a method to compute high-quality weights within a closed domain for image warping. The property of C2 continuity in weights is guaranteed by the carefully formulated basis functions. The efficiency of our algorithm is ensured by a closed-form formulation of the computation for weights. The cost of inserting a new handle is only the time to evaluate the distances from the new handle to all other sample points in the domain. A virtual handle insertion algorithm is developed to allow users to freely place handles within the domain while preserving the satisfaction of all expected criteria on weights for linear blending. Experimental examples for real-time applications are shown to demonstrate the effectiveness of this method. [ABSTRACT FROM PUBLISHER]

The research and development on a novel capacitive pressure sensor with an elastic pressure-sensitive element are presented in this paper. As a coaxial cylinder capacitor converter, it uses a circuit cylinder as its stationary anode and the piezo gauge's shell as its moveable cathode with the electrode distance of 0.5 mm. The device's principle and structure are given, and its mechanical characteristics are theoretically simulated by using the finite element software ANSYS. The read-out circuitry was developed. Various physical properties were experimentally tested. Results show that its capacitance is approximately linear to the subjected pressure. Its sensitivity is 0.0099 pF/Mpa, pressure measuring rang is 0–600 Mpa, By using the sensor's shell as its elastic sensitive element, its volume is reduced and its applications are increased, thus it can be used to measure the chamber pressures of various different caliber guns. In addition, its lower cost is helpful to promoting its applications. [ABSTRACT FROM PUBLISHER]

Aggregation operators have been used and studied for a long time. More than ten different types of means (including the arithmetic, geometric, and harmonic ones) were already studied 2000 years ago. Nevertheless, this topic has gained relevance in recent years. This is partly due to the increasing need of methods and operators for fusing information within computer programs. In this paper, we will present a personal view of the field, focusing on the application issues and on averaging aggregation operators. We will point out some research topics and open lines for future research. [ABSTRACT FROM AUTHOR]

Object-oriented (OO) technologies have become widely adopted in enterprise applications due to the additional functionality and flexibility they provide to these applications. At the same time, however, OO technologies also require significant amounts of computational power to support, greatly impacting the performance and scalability of such applications. A very popular solution to mitigate this problem is object caching. In this paper, we show how the application of object caching maps into an optimization problem. In particular, we focus on the design-time decision of determining which objects should be candidates for caching. Choosing the cacheable objects is an important decision since it can have a significant impact on application performance. We formulate this problem as a linear integer program and present a heuristic solution approach. We also demonstrate, through a set of experiments, that our heuristic provides solutions that are reasonably close to optimal. Our contribution is a model and an efficient solution approach for this model that can help application developers to make more informed cacheability decisions and thereby improve application performance and scalability. [ABSTRACT FROM PUBLISHER]

This paper presents a complete system capable of synthesizing a large number of pixels that are missing due to occlusion or damage in an uncalibrated input video. These missing pixels may correspond to the static background or cyclic motions of the captured scene. Our system employs user-assisted video layer segmentation, while the main processing in video repair is fully automatic. The input video is first decomposed into the color and illumination videos. The necessary temporal consistency is maintained by tensor voting in the spatio-temporal domain. Missing colors and illumination of the background are synthesized by applying image repairing. Finally, the occluded motions are inferred by spatio-temporal alignment of collected samples at multiple scales. We experimented on our system with some difficult examples with variable illumination, where the capturing camera can be stationary or in motion. [ABSTRACT FROM AUTHOR]

Terahertz irradiation and sensing is being applied for the first lime to a wide range of fields outside the traditional niches of space science, molecular line spectroscopy, and plasma diagnostics. This paper surveys some of the terahertz measurements and applications of interest in the biological and medical sciences. [ABSTRACT FROM AUTHOR]

In recent years, enterprise service bus (ESB) has become a favorable adoption as a technology category in the IT industry as it provides secure and guaranteed delivery of services. The elasticity of Enterprise Service Bus (ESB) enables numerous applications to exchange information makes it a significant middleware layer responsible for transferring information in a Service-Oriented Architecture (SOA). ESB is presently the utmost promising tactic for the integration of business applications in distributed and diverse environments. It also offers essential infrastructure support for transforming messages or data, intelligent routing, and protocol transformation. The idea of ESBs emerged from the requirements to move out from traditional integration patterns, that becomes difficult to manage with the passage of time. Our study aim is to understand and provide ongoing research topics, challenges and future directions concerning ESB applications. A systematic mapping study (SMS) is therefore implemented to categorize the selected papers into the following classification: contribution type, ESB applications, research type and their approaches. We have extracted a total of twenty-two papers for this systematic study and they are classified according to defined criteria. The findings of this SMS are discussed and researchers were provided with suggestions on possible directions for future research.

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, we also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.

Software maintenance is the dominant factor contributing to the high cost of software. In this paper, the software maintenance process and the important software quality attributes that affect the maintenance effort are discussed. One of the most important quality attributes of software maintainability is the stability of a program, which indicates the resistance to the potential ripple effect that the program would have when it is modified. Measures for estimating the stability of a program and the modules of which the program is composed are presented. and an algorithm for computing these stability measures is given. An algorithm for normalizing these measures is also given. Applications of these measures during the maintenance phase are discussed along with an example. An indirect validation of these stability measures is also given. Future research efforts involving application of these measures during the design phase, program restructuring based on those measures, and the development of an overall maintainability measure arc also discussed. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario.

Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions.

In this paper, a review of the main ground-penetrating radar (GPR) applications, technologies, and methodologies used in Italy is given. The discussion has been organized in accordance with the field of application, and the use of this technology has been contextualized with cultural and territorial peculiarities, as well as with social, economic, and infrastructure requirements, which make the Italian territory a comprehensive large-scale study case to analyze. First, an overview on the use of GPR worldwide compared to its usage in Italy over the history is provided. Subsequently, the state of the art about the main GPR activities in Italy is deepened and divided according to the field of application. Notwithstanding a slight delay in delivering recognized literature studies with respect to other forefront countries, it has been shown how the Italian contribution is now aligned with the highest world standards of research and innovation in the field of GPR. Finally, possible research perspectives on the usage of GPR in Italy are briefly discussed. [ABSTRACT FROM PUBLISHER]

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

In this paper, the integration of Internet of Things (IoT) regarding recent technology is discussed in a healthcare perspective. Various healthcare devices have emerged in recent years, allowing individuals to diagnose and manage their health conditions easily at home. However, the diagnostic utilization of IoT healthcare services is not yet fully integrated with the Internet. For this reason, people still have to go, for example, through a hands-on sampling process to self-analyze their urine, which is the same for inspectors in hospital labs. Therefore, with the exception of people suffering from certain diseases (e.g., diabetes), there are only a few who check their own health status due to the inconvenience. In this paper, sensor-based urine and feces separating toilet proposes a categorizing method for the user's toilet, which applies various scenarios and analyses of the urine components using long-range wireless communication technology to transmit data from the smart toilet to the user.

This paper presents a system based on ontologies for the definition of alarms in sensor systems. Although we consider that the ontology and the system are interesting themselves, we have a special impact on their experience with the software tools used and the technology related to the Semantic Web. This approach is intended to illustrate the process for those readers not familiar with this field and who can consider the use of ontologies for their next developments. An ontology has been designed in the OWL language, complemented with Semantic Web Rule Language rules and search elements in the Semantic Query-enhanced Web Rule Language. The set has offered satisfactory results in simulation. Other interesting contributions of this paper are: a survey of the available literature in the field of the use of Semantic Web technologies and ontologies for the detection of events from data obtained from sensors and a study on the tools and vocabularies to be used to create a system that interfaces with ontologies and an educational method-reporting our experience-to help university students understand this topic.

The past decade has seen the emergence of Vehicular Clouds as a topic of research in its own right. Vehicular clouds were inspired by the reality of present-day vehicles featuring an impressive array of on-board compute, storage and sensing capabilities and by the insight that these on-board capabilities are a vast untapped resource that, at the moment, is wasted. Harvesting these vehicular resources and putting them to work in a meaningful and productive way promises to have a significant and lasting societal impact. One of the defining ways in which vehicular clouds differ from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where reasoning about fundamental performance metrics becomes very challenging. Given that vehicular clouds and their numerous variants have becomes a very active field of research, this seems to be a good moment to survey the state of the art of vehicular cloud research. In this survey, we adopt an inclusive rather than an exclusive definition of various flavors of vehicular clouds. Vehicular clouds, in their many incarnations, have a huge array of potential applications. In order to bring this potential to fruition, significant research challenges have to be overcome. With this in mind, this survey identifies promising applications and draws attention to a number of research challenges facing the vehicular clouds community at large. [ABSTRACT FROM AUTHOR]