Overview Of Architecture Research Articles

Customized Software Testing Framework for Web Applications

Software testing is gaining more attention and investments in the IT companies. We have both manual and automated approaches, considering the dynamic of the project the appropriate one is chosen. There have been presented and used several frameworks and tools for this purpose. However, the outcome is that a single tool does not cover all the testing needs of IT systems. In this paper, we will present a customized framework based in Selenium that tackle this gap within the IT systems where several modules with many cross function are involved. We will present the architecture overview and a case study

The Use of a Modelling & Simulation Tier by the EMULSION IoT Platform

This paper presents some design aspects of the EMULSION IoT platform, developed as a typical example of the horizontal IoT platforms. The architectural overview and multi-tiered structure of the platform are described, with special attention being paid to its modelling & simulation tier as a novel architectural element proposed for inclusion in similar IoT platforms. Used to model cyber-physical-social (CPS) objects and IoT services, along with their attributes and temporal/spatial/event characteristics, this tier is also utilized to simulate the actual provision of IoT services in order to determine the optimal configuration of the platform in each particular use case, by solving complex optimization tasks. Examples of such tasks are presented in the paper along with some results obtained to date.

On the Performance of GRASP-Based Feature Selection for CPS Intrusion Detection

Cyber-Physical Systems (CPS) are the basis for the world’s critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cybersecurity. Aside from traditional information systems threats, CPS face new challenges due to the heterogeneity of devices and protocols, as well as its strong reliability requirements. In this work, we provide a brief overview of the CPS architecture and applications and describe the security challenges in the three CPS layers of perception, transmission, and application. Besides, we discuss how feature selection (FS) may improve intrusion detection performance. In particular, we evaluate how metaheuristic approaches can improve classification performance in CPS perception, transmission, and application layers. Our results reveal that (i) Greedy Randomized Adaptive Search Procedure (GRASP) outperforms traditional filter-based methods, and (ii) using the proposed enhanced approaches in GRASP construction and local search phases can enhance the average F1-Score of five classifier algorithms.

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

IT program management challenges: insights from programs that ran into difficulties

The use of Information Technology (IT) to drive organizational change has gained momentum in both for-profit and not-for-profit organizations, and currently culminates in a vivid discussion on what many call “Digital Transformation”. It is not surprising then that practitioners seek guidance on how to manage such transformation. Professional bodies have addressed this need by issuing best practice standards for Program Management (PgM), but we know little about their value in managing programs in general and IT programs in particular. Academic research on IT PgM is still in its very infancy. Taking this as motivation, we have investigated the challenges that managers faced in five IT programs that encountered problems. Our analysis reveals a set of management issues and shortcomings including, among others, a lack of architectural overview, difficulties in dealing with scope changes, stakeholder interest, diverse business groups and cultures, as well as a lack of internal PgM competences and unclear management responsibilities. In this paper, based on our findings, we present a first checklist for managing IT programs across their life cycle. Though still tentative and not necessarily comprehensive, we were able to confirm that our checklist provides relevant guidance for managing IT programs in practice.

An enhanced cross‐layer solution for IFC applications using HTS systems at Q/V‐band

The Q/V band communication systems open new avenues for next generation of high-throughput satellite (HTS) systems. More specifically, they are attractive due to their high bandwidth for user and gateway links. Having a wide bandwidth at Q/V band makes it permissible to offload the links between the satellite and its earth station hubs (gateway) from the Ka band to the Q/V band. This enables additional bandwidth available for HTS feeder links and user links of specific market verticals, which in turn results in reduction of cost per bit for in-flight connectivity applications to provide WiFi capability on the aircraft. This study provides a deep overview of network architecture at the ground gateway infrastructure with respect to the network management system (NMS) to optimize network traffic utilization. We have looked at a gateway constellation platform which optimizes the actual cost-efficient solution for deploying routing user traffic among the networks. In addition, the study discusses the elements involved in the NMS which interfaces with the gateway platform. The overall NMS platform design and capabilities are discussed as they provision the network service parameters throughout the entire operation. The proposed solution integrates the physical antenna, antenna control unit and the power supply into one bundle on the aircraft. This will result in approximately 10% throughput increase compared to conventional methods that do not deploy N+P gateway architecture. The proposed method has enhanced spectrum efficiency along with the NMS unit that has been discussed in detail. Finally, a wideband and multibeam antenna is introduced as an essential part of the solution followed up by simulation results.

Security Considerations to Enable Time-Sensitive Networking Over 5G

The evolution of 5G is set to bring about radical deployments in new sectors and novel use cases in the industrial, automotive, and retail segments. Moreover, the guarantee of providing ultra-high reliability and low latencies across the 5G bearer starting from 3GPP Release 16 along with the support of the Ethernet Protocol Data Unit (PDU), opens a plethora of time-sensitive networking applications enabled until now only over wired connections to over 5G. This progression also brings about challenges to end-to-end security for user data and for addressing safety risks due to hazards resulting from functional insufficiencies or foreseeable misuse by persons or rogue devices. In this paper, on one hand, we provide an overview of 5G architecture - take a deep dive into its built-in security mechanisms, and the User Plane Function, and on the other hand, we introduce principles of time-sensitive networking (TSN). We then bring about the correlation of the time-sensitive networking over wired (Ethernet) and wireless (5G) topology and draw the focus on the TSN-related assets and their corresponding possible vulnerabilities in the system and finally, propose approaches to address them.

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.

Smart Health Systems Components, Challenges, and Opportunities

Smart health is a relatively new paradigm where information and communication technology is utilized to improve health care and medical services. In this article, we provide a literature-based overview of smart health systems, their components, architecture, technologies, benefits, applications, challenges, and opportunities. In addition, we discuss the potential benefits of big data, data analytics, artificial intelligence (AI), and machine learning (ML) in smart health systems. Moreover, we discuss the challenges as well as the open research issues that need further investigation to facilitate the implementation of smart health systems.

Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions.

This paper provides a comprehensive literature review of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses. We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.

Optimized convolutional neural network architectures for efficient on-device vision-based object detection

Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.

Overview of the main powertrain architectures for hybrid and electric vehicles

The research for alternative solutions to assist the propulsion, fuel converters and energy storage systems (ESS) in vehicular applications has become the focus of many institutions and mainly in the automotive industry, aiming to reduce the impacts caused by the emission of gases in the exhaust pipe and to improve energy efficiency in the worldwide vehicle fleet. Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV) are currently a reality and meet this requirement to build a greener and less polluting society. In this context, this paper describes the operational characteristics of the different powertrain architectures employed in hybrid electric vehicles, including series, parallel and series-parallel topologies, as well as battery-powered and fuel cell electric vehicles. Finally, some of the elementary issues facing these advanced vehicular technologies, including the challenges for market penetration are highlighted.

Wide-field mosaics of the corneal subbasal nerve plexus in Parkinson\u2019s disease using in vivo confocal microscopy

In vivo confocal microscopy (IVCM) is a non-invasive imaging technique facilitating real-time acquisition of images from the live cornea and its layers with high resolution (1–2 µm) and high magnification (600 to 800-fold). IVCM is extensively used to examine the cornea at a cellular level, including the subbasal nerve plexus (SBNP). IVCM of the cornea has thus gained intense interest for probing ophthalmic and systemic diseases affecting peripheral nerves. One of the main drawbacks, however, is the small field of view of IVCM, preventing an overview of SBNP architecture and necessitating subjective image sampling of small areas of the SBNP for analysis. Here, we provide a high-quality dataset of the corneal SBNP reconstructed by automated mosaicking, with an average mosaic image size corresponding to 48 individual IVCM fields of view. The mosaic dataset represents a group of 42 individuals with Parkinson’s disease (PD) with and without concurrent restless leg syndrome. Additionally, mosaics from a control group (n = 13) without PD are also provided, along with clinical data for all included participants.

The neural signatures of social hierarchy-related learning and interaction: A coordinate- and connectivity-based meta-analysis

Numerous neuroimaging studies have investigated the neural mechanisms of two mutually independent yet closely related cognitive processes aiding humans to navigate complex societies: social hierarchy-related learning (SH-RL) and social hierarchy-related interaction (SH-RI). To integrate these heterogeneous results into a more fine-grained and reliable characterization of the neural basis of social hierarchy, we combined coordinate-based meta-analyses with connectivity and functional decoding analyses to understand the underlying neuropsychological mechanism of SH-RL and SH-RI. We identified the anterior insula and temporoparietal junction (dominance detection), medial prefrontal cortex (information updating and computation), and intraparietal sulcus region, amygdala, and hippocampus (social hierarchy representation) as consistent activated brain regions for SH-RL, but the striatum, amygdala, and hippocampus associated with reward processing for SH-RI. Our results provide an overview of the neural architecture of the neuropsychological processes underlying how we understand, and interact within, social hierarchy.

DNPSOUP: A simulation software package for dynamic nuclear polarization

Dynamic Nuclear Polarization Simulation Optimized with a Unified Propagator (DNPSOUP) is an open-source numerical software program that models spin dynamics for dynamic nuclear polarization (DNP). The software package utilizes a direct numerical approach using the inhomogeneous master equation to treat the time evolution of the spin density operator under coherent Hamiltonians and stochastic relaxation effects. Here we present the details of the theory behind the software, starting from the master equation, and arriving at characteristic operators for any section of density operator time-evolution. We then provide an overview of the DNPSOUP software architecture. The efficacy of the program is demonstrated by simulating DNP field profiles on small spin systems exploiting both continuous wave and time-domain DNP mechanisms. Examples include Zeeman field profiles for the solid effect, Overhauser effect, and cross effect, and microwave field profiles for NOVEL, off-resonance NOVEL, the integrated solid effect, the stretched solid effect, and TOP-DNP. The software should facilitate a better understanding of the DNP process, aid in the design of optimized DNP polarizing agents, and allow us to examine new pulsed DNP methods at conditions that are not currently experimentally accessible, especially at high magnetic fields with high-power microwave pulses.

Research on the Overview of Image Processing Architecture of Computer Based Deep Neural Network Accelerator

DNN algorithm still has many shortcomings in the process of operation, which need to be further solved. Specifically, there is more data reuse, and the repeated access of global cache takes up more resources and computation, thus reducing the efficiency of operation. Based on this, this paper first analyses the research status and value of the DNN accelerator, then studies the image processing architecture of the DNN accelerator, and finally gives the computer DNN model and acceleration algorithm analysis.

"Overview of Blockchain Technology: Consensus, Architecture, and Its Future Trends"

The blockchain technology that underpins Bitcoin has lately gained a lot of traction. Blockchain is a decentralized transaction ledger that is immutable. Blockchain applications are emerging in a variety of fields, including financial services, reputation systems, and the Internet of Things (IoT), among others. Nonetheless, concerns with blockchain knowledge, like as security or scalability, have yet to be solved. Bitcoin can only process 7 transactions per second, making it unsuitable for highfrequency trading. Larger blocks, on the other hand, imply more storage capacity and less network transfer. Because fewer individuals desire to own such a large blockchain, this will ultimately lead to centralization. This article provides a comprehensive description of blockchain technology. The researcher first provides an overview of blockchain architecture and then compares many commonly utilized consensus algorithms in different blockchains. A brief summary of technical difficulties and current advances is also included. They also highlight possible future blockchain developments.

Organic/Inorganic Hybrid Fibers: Controllable Architectures for Electrochemical Energy Applications.

Organic/inorganic hybrid fibers (OIHFs) are intriguing materials, possessing an intrinsic high specific surface area and flexibility coupled to unique anisotropic properties, diverse chemical compositions, and controllable hybrid architectures. During the last decade, advanced OIHFs with exceptional properties for electrochemical energy applications, including possessing interconnected networks, abundant active sites, and short ion diffusion length have emerged. Here, a comprehensive overview of the controllable architectures and electrochemical energy applications of OIHFs is presented. After a brief introduction, the controllable construction of OIHFs is described in detail through precise tailoring of the overall, interior, and interface structures. Additionally, several important electrochemical energy applications including rechargeable batteries (lithium‐ion batteries, sodium‐ion batteries, and lithium–sulfur batteries), supercapacitors (sandwich‐shaped supercapacitors and fiber‐shaped supercapacitors), and electrocatalysts (oxygen reduction reaction, oxygen evolution reaction, and hydrogen evolution reaction) are presented. The current state of the field and challenges are discussed, and a vision of the future directions to exploit OIHFs for electrochemical energy devices is provided.

A Survey-Based Study of Students’ Expectations vs. Experience of Sustainability Issues in Architectural Education at Wroclaw University of Science and Technology, Poland

Sustainability is currently one of the biggest concerns in the field of architecture and civil engineering. The presented study elaborates on the students’ expectations vs. experience of sustainable and ecological design in their architectural education. Students were surveyed after the interdisciplinary Hybrid Factory Design (HFD) course carried out at the Faculty of Architecture WUST, Poland. Respondents were asked to anonymously fill in a two-part online questionnaire in the last week of the summer semester of the academic year 2020/2021. The questionnaire was composed of 30 compulsory single-choice questions and 8 optional open questions. The single-choice questions were prepared using a five-point Likert scale, ranging from 1 (negative answer) to 5 (positive answer). Additionally, the Expectation Fulfilment Rate (EFR)—an original tool developed by the authors—was used to assess students’ expectations. The conducted survey revealed a significant disproportion between students’ expectations and experience regarding sustainable and ecological design aspects. There are also knowledge gaps in certain areas that should be addressed. Topics related to urban planning, green areas design, renovation and adaptation are not sufficiently represented in the curriculum. Moreover, it is essential to provide students with a broad, cross-disciplinary overview of sustainable architecture to deepen their understanding of different design aspects.

Military Architecture and the Four-Spāhbed System for Defense of the Sasanian Empire (224-651 CE)

This article examines Sasanian military architecture with respect to its integration with the four-region Spāhbed system (Ādurbādagān-Spāhbed, Xwarāsān-Spāhbed, Xwarbārān-Spāhbed and Nēmrōz-Spāhbed) for defending the empire. Following an overview of Sasanian military architecture within Iran, the article examines the Darband wall of the Caucasus in the context of the office of the Ādurbādagān-Spāhbed facing the empire’s north and northwest (Ādurbādagān, Media Atropatene corresponding with the historical Azerbaijan in Iran’s northwest), the Tammisha and Gorgan wall systems of the Xwarāsān-Spāhbed facing the nomadic warrior peoples of the Central Asia, the military architecture of the Xwarbārān-Spāhbed facing the western (Romano-Byzantine) frontiers, and the Khandaq-e Shapur of the Nēmrōz-Spāhbed facing the southwest, notably raiders from the Arabian Peninsula threatening the empire’s southwest marches.