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In the data-driven world, data is created in huge volume and then analyzed by several organizations to get benefit from them. Smart city is one of the examples to use big data to offer improved services for its resident and tourist. However, some countries face certain obstacles to analyze the big data integration for sustainability in smart city development. Therefore, the purpose of this research paper is to identify and analyze the significant barriers related to sustainable smart city development. To accomplish this objective, fourteen barriers of big data analytics are selected through the combined approach of literature review and expert input. After that, these barriers are evaluated using the best worst method for obtaining deeper insights. The result of this study reveals that the most significant barrier is ‘lack of technologies for BDA’, ‘lack of BDA framework’, ‘nature of big data’, and ‘low availability of analytics platforms for big data’. These barriers need to address on priority to develop a sustainable smart city. This study is helpful to the urban planner, government, and consultancy agencies to decide on the adoption of BDA for sustainable smart city development. Further, they can also optimize their resources in the best possible manner to achieve the sustainable development of the existing smart cities.

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.

In order to ensure the security of logistics information and to query information quickly and efficiently, using searchable encryption algorithms, combined with the characteristics of the blockchain, a searchable and encrypted logistics information blockchain data query algorithm is proposed. First, the logistics information is divided into multiple data files, encrypted with an asymmetric searchable encryption algorithm, and then stored in the cloud server. The keyword index value is extracted from each data file and uploaded to the blockchain. This solution can be used at any time Update and query data. Finally, analyze the correctness, completeness and safety of the scheme of this article, which proves the feasibility of this scheme.

When performing endoluminal surgery inside the colon using a flexible endoscopic robot, there is a problem of losing visualization and task space due to the collapsing of the surrounding wall. This happens mainly because of the intra-abdominal pressure and peristalsis of the colon. Although insufflation is commonly used for expanding the colon, it does not function when a full-thickness incision is created on the colon wall. To support the collapsing colon and ensure sufficient visualization and task space even with a hole, we developed a deployable colon support structure (CSS) that can be seamlessly adapted to the existing procedures. While the CSS is designed to be small and flexible enough to pass through an endoscopic channel that can be tortuous, it becomes steady enough to hold the collapsing/squeezing colon after being deployed. Also, the CSS is collapsible after task completion, for retraction through an endoscopic channel. Through the ex-vivo and in-vivo studies with a swine, we have successfully demonstrated the feasibility of supporting the colon wall during endoluminal interventions with the CSS. We confirmed that the CSS was easily deliverable and deployable and the created space was large enough to perform surgical tasks using robotic arms. National Research Foundation (NRF) Published version This work was supported by the National Research Foundation (NRF) Singapore under Grant NRFI2016-07

Made available in DSpace on 2022-04-29T08:29:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration. Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira Department of Electrical Power Engineering Tishreen University School of Electrical and Computer Engineering National Technical University of Athens Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira

Recently, vehicle-to-infrastructure (V2I) communication-based scheduling schemes for connected and automated vehicles (CAVs) on highway on-ramps have received attention because the schemes assign optimal merging times to CAVs based on their criteria, such as first-in first-out order or minimizing CAV total travel time. However, these schemes cause traffic congestion due to the criteria when the vehicle inflow traffic becomes high. To mitigate traffic congestion, a scheduling scheme should assign merging times to CAVs to decrease road vehicle density. In this paper, we propose a scheduling scheme for autonomous vehicle highway merging with an outflow traffic and fairness analysis. We formulate a multiobjective function that achieves both outflow traffic maximization and fairness in assigned merging times. Since more vehicles pass a highway on-ramp as highway on-ramp outflow traffic increases, the proposed scheme decreases vehicle density on roads by maximizing the outflow traffic. Furthermore, the proposed scheme distributes the vehicle density on both the main and ramp roads by fairly assigning their merging times. Simulation results show that the proposed scheme mitigates the potential traffic congestion of previous scheduling schemes. Furthermore, the proposed scheme improves outflow traffic from highway on-ramps by more than 200 veh/h compared with the previous scheduling schemes.

The water-dropping scheme plays an important role in the aerial firefighting mission. When a wildfire occurs, the main problem faced by the aircrew is how to choose the appropriate water-dropping scheme (speed, height and trajectory) to achieve the optimal firefighting effect. However, so far, the crew members can only rely on the existing experience to generate the scheme, which lacks the corresponding theory method support. To solve above problem, this paper proposes a fast optimization method of water-dropping scheme for fixed-wing firefighting aircraft. Through the feature extraction of grid distribution, the agent model between aircraft water-dropping scheme and water distribution polygon is built taking advantage of neural network. Meanwhile, the corresponding extinguishing efficiency evaluation model is established with discrete points of fire area. Based on these researches, the combined genetic algorithm is used for searching the optimal water-dropping scheme. Finally, the feasibility and rapidity of the method are demonstrated through the application of examples. It is also shown that the method can optimize the water-dropping scheme quickly instead of aircrew experience and play a role of auxiliary decision support for the firefighting mission of fixed-wing firefighting aircraft.

The complexity of power systems is increasing as new generating units are added to power systems in order to supply power to the growing economies. This has resulted in further research into the generator maintenance scheduling (GMS) problem which seeks to ensure optimal preventive maintenance scheduling that is effective and reliable. This research is focused on developing a generator maintenance schedule using a tri-objective model. The GMS tri-objective model is solved using two solution methodologies. The first is an exact solution method using mathematical modelling software, Advanced Interactive Multidimensional Modelling System (AIMMS). The second solution method is a recently developed metaheuristic algorithm called Exchange Market Algorithm (EMA). Results show that the tri-objective model finds a trade-off solution of the individual solution methods. The metaheuristic algorithm gives a better solution for larger optimization problems.

Insect detection and counting constitute a considerable challenge in the field of agriculture. However, among various biotic issues of agricultural production, pest infestation is a major challenge, with the humid environment surrounding the crops encouraging the survival and proliferation of pests. In addition, electronic traps need protection, especially from rain. This study describes the design and development of a prototype for an automatic pest sampling and detection system for agricultural crops. To the best of our knowledge, the proposed system is the first motorized automatic trap developed to handle monitoring operations in two directions of precise movement (i.e., clockwise and counterclockwise) and cover four directions of insect sampling that provides additional details of insect infestation direction. A square-shaped sticky box was designed, and an optical sensor was attached to a scalable arm. The movements of the sticky box and the camera arm were generated by motors. Preprocessing was conducted by using morphological operations, whereas insect detection and counting were implemented by an algorithm of connected components labeling that applied by using MATLAB image processing toolbox. Different kernel functions, such as disk, diamond, square, and sphere, were used as matching functions for the insect detection and counting algorithm. The average accuracy of the highest sphere kernel was 85.2%. Test results of the hardware show the reliability, flexibility, and system protection of the automatic system to provide accurate movements in two degrees of freedom.

The multiple signal classification algorithm (MUSICAL) is a statistical super-resolution technique for wide-field fluorescence microscopy. Although MUSICAL has several advantages, such as its high resolution, its low computational performance has limited its exploitation. This paper aims to analyze the performance and scalability of MUSICAL for improving its low computational performance. We first optimize MUSICAL for performance analysis by using the latest high-performance computing libraries and parallel programming techniques. Thereafter, we provide insights into MUSICAL’s performance bottlenecks. Based on the insights, we develop a new parallel MUSICAL in C++ using Intel Threading Building Blocks and the Intel Math Kernel Library. Our experimental results show that our new parallel MUSICAL achieves a speed-up of up to 30.36x on a commodity machine with 32 cores with an efficiency of 94.88%. The experimental results also show that our new parallel MUSICAL outperforms the previous versions of MUSICAL in Matlab, Java, and Python by 30.43x, 2.63x, and 1.69x, respectively, on commodity machines.

It is usual in many applications that a large fraction of data pages is, once created, actively updated for a while, and then eventually no longer updated. In flash storage, these frozen pages tend to spread over physical flash blocks and thus are repeatedly relocated upon garbage collection, exacerbating the write amplification. To address the problem of scattered frozen pages, we propose the interface, which allows users or applications to inform the flash storage of frozen pages explicitly. Upon receiving the call, FTL clusters and isolates frozen pages into a physically separate space so that they are no longer relocated upon garbage collection. Experimental results confirm that can reduce the write amplification factor from 25% to 40% of the conventional SSDs. The scheme is novel in the way it opens up a model case of physical tuning knobs in flash storage.

The management of the uncertainty existing in any production system is fundamental to define machine scheduling models that allow programming production instances attached to the real world. In this research, a generalized decision-making system is developed for the management of uncertainty existing in flow shop machine scheduling models. The system assessment the uncertainty existing in internal and external factors that influence the decision-making process of production programming experts, and that is decisive in a final machine scheduling. The system is based on the combination of the Fuzzy Hierarchical Analysis Process, a membership analysis, and an Artificial Neural Network (ANN). The system allows to concentrate the experience of experts in machine scheduling and generalize their knowledge. The efficiency of the system is verified with a Fuzzy Hierarchical Analysis Process Model, the “ANN toolbox” preloaded in MATLAB and variety of structures of an Artificial Neural Network. The results are validated in an industrial application and the system is contrasted against an expert. The results show the efficiency of the system as it defines and predicts the final machine scheduling of production instances; the joint assessment of variables that add uncertainty to the production system allowed to reduce delays in product deliveries.

Learning action policy for autonomous agents in a decentralized multi-agent environment has remained an interesting but difficult research problem. We propose to model this problem in a contextual bandit setting with delayed reward signals, in particular an individual short-term reward signal and a shared long-term reward signal. Our algorithm utilizes an approach with reward oracles to directly model these delayed reward signals and also relies on a learning scheme benefiting from the sampling guidance of an expert-designed policy. This algorithm is expected to apply to a wide range of problems, including those with constraints on accessing state transitions and those with implicit reward information. A demonstration, deployed by deep learning regressors, shows the effectiveness of the proposed algorithm in learning offensive action policy in the RoboCup Soccer 2D Simulation (RCSS) environment against a well-known adversary benchmark team compared to a baseline policy.

Skyline query has been studied extensively and a significant number of skyline algorithms have been proposed, mostly attempt to resolve the optimisation problem that is mainly associated with reduction in the processing time of skyline computations. While databases change their states and/or structures throughout their lifetime to reflect the current and latest information of the applications, the skyline set derived before changes are made towards the initial state of a database is no longer valid in the new state/structure of the database. The domination relationships between objects identified in the initial state might no longer hold in the new state. Nonetheless, computing the skylines over the entire new state/structure of the database is inefficient, as not all pairwise comparisons between the objects are necessary to be performed. In tackling the above issue, this paper proposes a solution, named $\Delta $ Skyline, which aims at avoiding unnecessary skyline computations when a database changes its state and structure due to a data definition operation(s) (add or remove a dimension(s)). This is achieved by identifying and retaining the prominent dominance relationships when pairwise comparisons are performed; which are then utilised in the process of computing a new skyline set. $\Delta $ Skyline consists of two optimisation components, namely: $\Delta ^{+}$ Skyline which derives a new skyline set when a new dimension(s) is added to a database and $\Delta ^{-}$ Skyline which derives a new skyline set when an existing dimension(s) is removed from a database. To make our solution more useful, it is applied on a database with incomplete data. Extensive experiments have been conducted to evaluate the performance and prove the efficiency of our proposed solution.

In this paper, we develop a privacy-preserving UAV system that does not infringe on the privacy of people in the videos taken by UAVs. Instead of blurring or masking the face parts of the videos, we want to exquisitely modify only the face parts so that the people in the modified videos still look like humans, but they become anonymous. Doing so, the semantic information of the videos can be preserved even with the anonymization. Specifically, based on the latest generative adversarial network architecture, we propose a deep learning-based face-anonymization scheme so that each modified face part looks like the face of a person who does not actually exist. The trained face-anonymizer is then mounted on the UAV system we have implemented. Through experiments, we confirm that the developed privacy-preserving UAV system anonymizes UAV’s first-person videos so that the people in the video are not recognized as anyone in the dataset used. In addition, we show that even with such anonymized videos, the perception performance required for performing UAV’s essential functions such as simultaneous localization and mapping is not degraded.

Many approaches have been proposed to teach the basic concepts of big data and artificial intelligence to K-12 students based on block-based programming languages, such as Scratch. Using these approaches, young students can easily experience big data and artificial intelligence through a drag-and-drop approach. However, it remains difficult for them to perform more complex tasks, such as directly collecting data from the web or exploiting custom-made machine learning algorithms. In this paper, we propose a novel Scratch extension that allows Scratch to communicate with text-based programming languages such as Python and JavaScript using WebSockets. Unlike other Scratch extensions, our extension greatly enhances the extensibility of Scratch given its use of “text-based visual blocks” so that messages can be freely exchanged through a minimum number of blocks. In order for students to use these blocks easily, the blocks are designed such that they can be used as if talking with a friend named “Tooee.” In order to show how this extension can help students create big data and artificial intelligence programs, we present eight example applications that students can easily implement. These are (1) Weather Forecast, (2) Top 5 Movies in Theaters, (3) COVID-19 Dashboard, (4) Saving Quiz Results to a CSV File, (5) Facial Image Classification, (6) Color Classification, (7) Object Classification, and (8) Handwriting Recognition. Our analyses and experimental results show that Tooee has several advantages over other educational environments.

We propose a high-speed convolutional neural network approach for weakly supervised localization (WSL) and weakly supervised object detection (WSOD). The proposed method, called center-of-features localization (COFL), performs localization of objects in a visual scene by combining both multi-label classification and regression for the number of instances of each class object. A modified Xception network architecture is used as the main feature extractor, and a classification-plus-regression loss function is used to perform the detection task. The method does not require bounding box annotations but only image labels and counts of the objects of each class in the image. This combination can produce a clear localization of objects in the scene through a masking technique between class activation maps (CAMs) and regression activation maps (RAMs). The proposed method was trained and tested on the PASCAL VOC2007 and VOC2012 datasets; it attained a mean average precision (mAP) of 47.0% and a correct localization CorLoc of 64.1% on PASCAL VOC2007 and a mAP of 42.3% and a CorLoc of 65.5% on PASCAL VOC2012 while performing object detection at a speed of ~50 fps. These results demonstrate that the network can perform object detection accurately in real-time using only image labels and object counts, which are inexpensive to annotate compared with the bounding box annotations typically employed in fully supervised object detection methods. The network far outperforms other weakly supervised methods and some fully supervised methods in terms of processing time while achieving fair accuracy.

Motor design can be said as multi-modal optimization problem, as many performances should be considered. In addition, a time-consuming finite element method (FEM) is required for accurate analysis of the motor, and such computational burden becomes worse when the FEM is applied to multi-modal optimization problem. In this paper, adaptive-sampling kriging algorithm (ASKA) is proposed to relieve the computation cost of multi-modal optimization problem. The ASKA utilizes kriging interpolation model with generated samples by Compact Search Sampling (CSS) and Exclusive Space-filling Method (ESM). The CSS improves the accuracy of the solutions by generating samples near the expected solutions, and the ESM guarantees the diversity of solutions by generating samples far from existing samples, avoiding solution-near area. Using CSS and ESM, the ASKA adjusts the number of samples effectively and reduces function call considerably. The superior performance of the ASKA was verified by mathematical test functions with complex objective function regions. To validate the feasibility of actual electric machines, the ASKA was applied to optimal design of permanent magnet assisted synchronous reluctance motors for electric vehicles and optimum design with diminished torque ripple is derived.

Software projects are not void from bugs when they are released, so the developers keep receiving bug reports that describe technical issues. The process of identifying the buggy code files that correspond to the submitted bug reports is called bug localization. Automating the bug localization process can speed up bug fixing and improve the productivity of the developers, especially with a large number of submitted bug reports. Several automatic bug localization approaches were proposed in the literature reviews which are based on the textual and /or semantic similarity among the bug reports and the source code files. Nevertheless, none of the previous approaches made use of the source code complexity despite its importance; as high complexity source code files have higher probabilities to be modified than the low complexity files and are prone to bug occurrences. To improve the accuracy of the automatic bug localization task, this paper proposes a Hybrid Bug Localization approach (HBL) that makes full use of textual and semantic features of source code files, previously fixed bug reports, in addition to the source code complexity and version history properties. The effectiveness of the proposed approach was assessed using three open-source Java projects, ZXing, SWT, and AspectJ, of different sizes. Experimental results showed that the proposed approach outperforms several state-of-the-art approaches in terms of the mean average precision (MAP) and the mean reciprocal rank (MRR) metrics.

Technology advancement in the last few decades allows large penetration of renewable energy resources in the distribution network (DN). The integration of such resources has shown a substantial impact on DN through power loss reduction and improved network reliability. Besides this, the existing protection system has encountered coordination challenges due to the bidirectional power flow, different types and capacity of generation sources, and changes in fault levels due to network operating modes (grid-connected or islanded). Such conditions may cause the relays to malfunction and imperil the effectiveness of the existing protection scheme. Therefore, an efficient and robust protection coordination scheme is imperative to avoid network reliability and stability issues to the grid. This review paper presents a comparative analysis of various protection techniques implemented to alleviate the impact of integrated resources into DN. Moreover, a comparison of classical and modified protection approaches in terms of advantages, shortcomings, and implementation costs is presented. The prime objective of this study is to highlight the prominence of utilizing user-defined programmable relays for modern DNs. Moreover, recommendations are presented by considering the application of user-defined relay characteristics that can be proved as a robust protection scheme to cope with the protection challenges in existing and future power systems developments.

The append-only write scheme of the log-structured file system (LFS), which does not permit in- place updates, is suitable for flash memory. Therefore, LFSs have been adopted to manage various flash-memory-based storage systems. However, LFSs amplify user write requests owing to segment cleaning and frequent metadata write operations. Hence, the IO performance is degraded and the life span of the flash memory is reduced. In particular, when each segment cleaning invokes checkpointing to store dirty metadata modified via block relocation, irrelevant dirty metadata are written by the checkpointing operation, and the metadata write overhead occupies a significant proportion of the segment cleaning latency. Hence, we propose segment cleaning journaling (SCJ), which logs the block relocation information of segment cleaning at a specific journal area without invoking checkpointing. We implemented and evaluated the proposed SCJ by modifying F2FS. Experimental results show that SCJ can improve file system performance by 1.13–1.63 times compared with normal segment cleaning.

In this paper, we explore Android applications’ file access characteristics, and find out that smartphone file accesses are different from traditional desktop applications in terms of the following aspects. 1) There exist a limited number of hot blocks, which are accessed consistently during the entire execution of an application. 2) Block accesses in Android are highly biased such that the top 20% blocks account for 80% of total accesses. 3) Hot blocks of the top 100 rankings are mostly involved in SQLite. 4) Unlike desktop applications, file accesses in Android applications are write-intensive. 5) In predicting future file accesses in Android applications, frequency is a better estimator than temporal locality. 6) The effect of traditional buffer cache is limited in Android as file I/O in Android has a lot of synchronous writes, which incurs immediate storage flushing. Based on these analyses, this paper presents the implication of buffer cache management in Android. Specifically, we add a small non-volatile write buffer and present how this write buffer can be managed efficiently. Experimental results show that the proposed scheme improves the storage write traffic by an average of 21.7% and a maximum of 48.1% compared to the conventional buffer cache system.

A novel design method for compact planar lowpass filter (LPF) with prominent roll-off rate and wide stopband suppression is proposed by using fragment-type structures (FTS) with multi-bit scheme. The proposed multi-bit FTS design has the potential to achieve higher LPF performance at higher optimization efficiency, without any prerequisite knowledge about the pivotal LPF structures needed in conventional LPF designs. The definition of multi-bit FTS and the implementation of LPF design with multi-bit FTS scheme are presented in detail. Compared to the LPF design with 1-bit FTS, the 2-bit FTS design may use smaller decision space, take fewer iterations in multi-objective optimization searching, and yet achieve sharper roll-off rate, higher stopband suppression and a wider stopband. For demonstration, planar LPFs on PCB using the 1-bit FTS and 2-bit FTS are designed and tested. The 2-bit FTS design yields a sharp roll-off rate of 123.3 dB/GHz, a relative stopband bandwidth of 165% referred to stopband suppression of 20 dB, and a high figure-of-merit of 21060. Effects of the geometry parameters of the 2-bit elements are further discussed to give some guide rule on using the 2-bit FTS design.

Blind signature is a special type of digital signature, the signer cannot see the specific content signed. However, blindness may cause users to abuse their rights. Partial blind signature allows the signer to embed pre-negotiated public information in the blind signature without losing blindness, which can prevent users from abusing their rights. Islam et al. presented an identity-based partial blind signature scheme and claimed that it is provable secure. However, in this paper we proved that the scheme is vulnerable against the tampering attacks with public information. We then proposed a new identity-based partial blind signature scheme, showed the security proofs under the assumption that the elliptic curve discrete logarithm problem (ECDLP) is difficult. The new scheme does not use pairing operations and enjoys less computation cost.

Blade tip timing (BTT) vibration measurement is a promising on-line blade monitoring method, but various uncertainties bring great challenge in engineering applications. Most existing works are based on the assumption of constant rotating speeds. However, rotating speed is hardly fixed under variable conditions. In this case, these uncertainties always become more serious. To deal with this problem, this paper proposes to investigate BTT measurement derivations in angular domain, instead of time domain. Firstly, this paper systematically analyzes the effects of variable rotating speed, static angle errors and translational blade motions on the accuracy of BTT vibration measurement. Then the corresponding calibration methods are presented by only using times of arrival (TOAs). In the end, Matlab/Simulink simulations are done to validate the proposed method under linear and quadratic variations of rotating speeds. The results demonstrate that BTT measurement deviation under low rotating speeds is more than those under high rotating speeds and BTT measurement deviation due to static angle errors is independent of rotating speeds. And the proposed TOAs-based calibration method can reduce BTT measurement deviation greatly under variable rotating speeds, compared with traditional methods. BTT measurement deviation due to static angle errors can be calibrated by using low rotating speeds and those due to translational blade motions are difficult to be calibrated under variable rotating speeds. Thus simulation results indicate great potential of the proposed method for practical applications of the BTT method.

Model-based approaches are essential for designing cyber-physical systems, which adopt the formal models to simultaneously form the specifications and enable the verification at an early stage. Aimed to model the complex structure and continuous-discrete hybrid behavior of cyber-physical systems, this paper mathematically defines a dynamic relational system so that the cyber-physical system can be regarded as dynamic relational systems in a hierarchical structure and each dynamical relational system is a triple of dynamic attributes, subsystems, and hybrid relations between attributes and subsystems. Every hybrid relation contains a tuple and a predicate to govern the system behaviors. By utilizing the dynamic relational system, a parametric abstraction is then performed to specify the design requirements and schemes. It can represent the structure and behaviors of multiple cyber-physical system design schemes in an integrated manner. With a mathematical foundation, the constructed relational models are beneficial for structural analysis and behavior verification. An implementation case of a friction-driven plate conveyor is presented to illustrate the design specification with relational models, and the connectivity analysis and behavior verifications are carried out to show the effectiveness and engineering practicability of the achieved models.

This paper presents the effect of irradiance change in solar photovoltaic (PV) array interconnection schemes under partial shading conditions (PSCs) for optimal selection of PV interconnection schemes in order to extract maximum power (MP) from the PV array. A $4{\times }4$ PV array is considered to examine the well-known PV interconnection schemes under different shading scenarios at distinct irradiance conditions. Simulation studies and experimental verification are carried out by considering a $30~W$ PV panel to analyse the performance of the PV array under various dynamic conditions. Comprehensive analysis in terms of fill factor, output power and mismatching power loss is presented by considering different interconnection schemes under various shading scenarios and irradiance levels. From the simulation studies and experimental verification, it is observed that the irradiance magnitude has a significant impact on the PV power generation from array interconnection schemes under PSCs wherein the performance characteristics of the array interconnection schemes vary with the variation in irradiance levels. It is also identified that the irradiance factor needs to be considered as a design variable while selecting the optimal PV array interconnection schemes for maximum power extraction under PSCs.

Deep convolutional neural networks (CNNs) are widely used to improve the performance of image restoration tasks, including single-image super-resolution (SISR). Generally, researchers are manually designing more complex and deeper CNNs to further increase the given problems’ performance. Instead of this hand-crafted CNN architecture design, neural architecture search (NAS) methods have been developed to find an optimal architecture for a given task automatically. For example, NAS-based SR methods find optimized network connections and operations by reinforcement learning (RL) or evolutionary algorithms (EA). These methods enable finding an optimal system automatically, but most of them need a very long search time. In this paper, we propose a new search method for the SISR that can significantly reduce the overall design time by applying a weight-sharing scheme. We also employ a multi-branch structure to enlarge the search space for capturing multi-scale features, resulting in better reconstruction on the textured region. Experiments show that the proposed method finds an optimal SISR network about twenty times faster than the existing methods, while showing comparable performance in terms of PSNR vs. parameters. Comparison of visual quality validates that the obtained SISR network reconstructs texture areas better than the previous methods because of the enlarged search space to find multi-scale features.

Traditional blockchains preserve everything, if verification is to avoid trusted third parties. The accumulation of outdated but undeletable data makes traditional blockchains non-scalable for practical applications. Stateless blockchains mitigate this problem via history deletion; however, existing stateless blockchains such as Mimblewimble are not quantum resistant as they rely on specific discrete-log-based cryptography to achieve history deletion. In this paper, we introduce the “Origami Store” (or O-store for short), as a general new approach to distributed data storage with a generic history deletion mechanism called “folding.” O-store is built using a hash function and an arbitrary signature scheme. Since the signature scheme is decoupled from the framework, O-stores are compatible with any quantum-resistant signature scheme by design. More importantly, O-store only needs to preserve one signature and one verification key per account (or owner) for any number of transactions (or updates), thereby mitigating the inefficiency of large quantum-safe signatures and verification keys. We prove the security of the basic O-store using the framework of Universal Composability (UC); in particular, O-store’s universal security is shown to hold for a generic consensus mechanism, to facilitate implementation from a wide range of decentralized consensus protocols. The Origami store is thus the first UC-secure efficient distributed data storage framework that supports plug-and-play consensus mechanisms for fully decentralized trustless operation. We demonstrate working implementations of Origami stores with three post-quantum signature libraries—qTESLA, Dilithium, and Picnic—to illustrate its scalability and practicality for real-world applications.

In the Internet-of-Things (IoT), different types of devices can co-exist within a network. For example, there can be cheap but inflexible devices and flexible devices in terms of radio frequency (RF) capabilities. Thus, in order to support different types of devices in different ways and improve throughput, we propose a multichannel random access scheme based on power-domain non-orthogonal multiple access (NOMA), where each flexible or dynamic device (DD) can dynamically choose one of multiple channels when it has a packet to send. In addition, since DDs need to learn the channel selection probabilities to maximize the throughput of DDs, we consider two-sided learning based on a multi-armed bandit (MAB) formulation where rewards are decided by learning outcomes at a base station (BS) to improve learning speed at DDs. Simulation results confirm that two-sided learning can help improve learning speed at DDs and allows the proposed NOMA-based random access approach to achieve near maximum throughput.

Since the GDPR was implemented in 2018, organizations that collect data from the EU residents are required to receive the user’s consent. Organizational measures to ensure that the organizations are compliant to the recently enacted GDPR are still abstract and ambiguous. Moreover, data subjects and controllers have demanded the practice of obtaining consent from organizations. By observing the case law and guidelines related to the GDPR provisions, we deduced four consent conditions. Then, we examined how online service provider’s websites are making efforts to implement the GDPR framework. For this, we identified key characteristics of these websites, such as the existence of consent buttons. In order to help the data subjects obtain consent, we proposed an automatic tool that can check the consent conditions by checking the websites. Our study examined 10,000 websites for 26 days using the Python libraries with the tool automatically crawling the website information and analyzes the HTML structure according to the specified conditions. In addition, this tool crawls the privacy policy of each website. Moreover, it automatically determines whether it meets the four consent conditions by calculating it according to the formula defined in the consent condition. To evaluate the tool’s accuracy, the researchers manually analyzed 500 websites and compared the manual analysis with the results of the tool’s automatic analysis. We found that this tool differentiates itself through qualitative comparisons with other GDPR meters.

ACKNOWLEDGMENT: The author would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/61), Taif University, Taif, Saudi Arabia. Accurate and fast fault monitoring system is important for photovoltaic (PV) systems to reduce the damage and energy loss associated with faults. This paper presents a fast fault detection and classification scheme for PV systems. The rate of change of the measured power at the array level is firstly used to detect and differentiate between shading and PV system short-circuit faults. Then, the measured cross-strings differential currents classify the fault type in the case of short-circuit faults. The proposed method accounts for intra-string, string-to-negative terminal, cross-string (string to string), and pole-to-pole short-circuit faults, as well as open circuit faults. Two current transducers per two strings are only required to apply the method leading to a significant reduction in the number of transducers/sensors compared to the literature, making it a cost-effective solution. The fault is detected and classified in 1 ms. A 400 kW PV system consisting of 4 arrays simulated using MATLAB/Simulink package is used for the evaluation process. Short-circuit faults are imposed at different conditions of mismatch levels and fault resistance values. Also, operation at reduced irradiance level is considered, and shading faults are simulated in different levels. The simulation results prove the ability of the proposed scheme to detect and differentiate between shading and short-circuit faults. The proposed method can correctly classify different fault types in almost all cases. The low implementation cost due to the lower number of required sensors and the fast-operating time (1 ms) boost the feasibility of the proposed method.

Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient methods become unsuitable to solve some problems. In this work, a new algorithm was introduced in order to reduce the long runtimes involved in the search of typical testors. The performance of the proposed algorithm was evaluated by means of several tests, which use both real-world and simulation data. MATLAB and Java language on Eclipse SDK platform were used to build the simulation dataset and to perform the tests, respectively. The runtimes achieved by the proposed algorithm were significantly shorter than those obtained by fast-BR and GCreduct (the two fastest algorithms) mainly when the latter ones exhibited excessively long runtimes.

Cortical connectivity analysis is a widely used method for understanding the causes of neurological disorders and related brain mechanisms. Although there exist numerous activity analysis toolboxes for functional near-infrared spectroscopy (fNIRS), there are only a few cortical connectivity analysis toolboxes. In 2019, we released a MATALB toolbox named OptoNet , which has helped researchers to analyze brain networks using fNIRS. In this study, we developed an advanced MATLAB toolbox, named OptoNet II, to add new features that overcome the shortcomings of OptoNet . With these new features, OptoNet II can efficiently analyze cortical connectivity according to brain region using any fNIRS channel sets and can present the results of two connectivity analyses with auto-thresholding based on surrogate tests. To evaluate the efficacy of the new functions, the finger-tapping task experiment was carried out before and after transcranial direct current stimulation (tDCS) in the primary motor area. OptoNet II can efficiently show the effects of tDCS on functional brain region connectivity, which has been difficult to confirm by conventional methods. In this article, we propose the OptoNet II as a useful and efficient toolbox for researchers who want to perform cortical connectivity analysis using fNIRS.

Regression testing plays an indispensable role in software maintenance, which refers to retest the software following modifications to determine whether the changes have introduced new faults. However, regression testing requires massive amounts of effort to achieve a high fault detection rate. To address this issue, the test case prioritization technique is used to improve the fault detection rate by adjusting the execution order of test cases. For model-based regression test case prioritization, existing approaches have been developed using the single aspect of model-related information extracted from the previous executed test cases. In this paper, a novel learn-to-rank technique is proposed to prioritize test cases by combining the multidimensional features of Extended Finite State Machine (EFSM) under test to improve fault detection rate. Specifically, our method utilizes the random forest algorithm to combine multiple existing heuristic prioritization methods. Detailed experiments are conducted to evaluate the proposed method’s performance in terms of Average Percentage Fault Detected (APFD). The experimental results show that the mean APFD value of our method reaches 0.884 for five subject EFSMs, which is 33.9% higher than the compared methods.

Air-writing recognition has received wide attention due to its potential application in intelligent systems. To date, some of the fundamental problems in isolated writing have not been addressed effectively. This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs). A robust and efficient hand tracking algorithm is proposed to extract air-writing trajectories collected by a single web camera. The algorithm addresses the push-to-write problem and avoids restrictions on the users’ writing without using a delimiter and an imaginary box. A novel preprocessing scheme is also presented to convert the writing trajectory into appropriate forms of data, making the CNNs trained with these forms of data simpler and more effective. Experimental results indicate that the proposed approach not only obtains much higher recognition accuracy but also reduces the network complexity significantly compared to the popular image-based methods.

Synthetic Aperture Radar (SAR) simulation is widely used for system design, processing techniques development, and mission planning. However, there is no readily available and free framework for SAR raw signal simulation, and many teams and organizations struggle with developing their own simulator from scratch, repeating work that others have done earlier. This work’s purpose is to create a simple, open-source SAR simulation framework that can be used for many purposes and is available for free to everyone. It is a ready tool for SAR processing techniques verification, and thanks to its open-source nature, it fosters collaboration between scientists, both on the simulation and the tool development. Moreover, as the simulation’s underlying mathematics is described in detail in this article, it may serve as a handbook for the radar’s simulation. The simulator was prepared as a stand-alone, multiplatform software working in the Matlab/Octave environment. It is available in an online software repository that allows others to contribute to the original code or to create forks of it. The simulator supports monostatic, bistatic, and multistatic configurations with shadowing. The surface model includes roughness using the modified Phong model, transparency, and complex reflectivity. The antenna pattern is defined with a general two-dimensional model. The article is concluded with the simulator’s demonstrations, including antenna pattern, Doppler shift, and various surface parameters. It is expected that this simulator will be widely adopted and improved by many collaborators.

Weakly-supervised object detection (WSOD) has attracted lots of attention in recent years. However, there is still a big gap between WSOD and generic object detection. The main barriers to the efficiency of WSOD are the ineffective data augmentations and inaccurate bounding box predictions. Given only image-level annotations, it is hard for WSOD to effectively utilize variant data augmentations and accurately regress the bounding boxes. Although a fully-supervised object detector can be trained using annotations generated from the weakly-supervised object detector, the performance is still severely limited due to the low quality of mined pseudo annotations. This paper proposes an efficient WSOD method with pseudo annotations (EWPA) to make better use of imperfect annotations. With the assistance of pseudo annotations, EWPA can effectively regress more accurate bounding boxes while the traditional WSOD can only locate the salient parts of an object. Furthermore, pseudo annotations can help design more complex data augmentations, driving the network to learn more discriminative feature representations. Extensive experiments are conducted on PASCAL VOC 2007 and 2012 datasets and validate the effectiveness of EWPA.

Image recognition is a popular and important research field of computer vision. Recently with the development of deep learning technology, image recognition performance has been improved significantly. However with multi-label images, recognizing individual category is a challenging task. In order to address the problem, we propose a Feature Disintegrator (FD) that decomposes co-occurred instance features of multi-label into individual categories. In experimental evaluation, proposed method achieves the gains of mean average precision (mAP) up to 18.67% and 29.05% over baseline networks in ML-MNIST and ML-CIFAR, respectively. It shows 5.76% and 6.65% higher mAP than baseline on PASCAL VOC-2007 and MS-COCO data set respectively.

A Behavioral Authentication (BA) system constructs a behavioral profile for a user and uses it to verify their identity claims. It is primarily used as a second factor in user authentication. A BA system starts with an initial database of user profiles, and uses a verification algorithm to accept or reject a verification request, that consists of a claimed identity and behavioral data by measuring the distance between the profile of the claimed identity and the presented behavioral data. As new users join the system, the size of profile database grows and for a user $u$ , the probability of other users with “close” profiles increases, and this results in an increase in the error probability of verification algorithm. We analyze this problem in distance-based verification systems, and introduce the notion of scalability of BA systems that requires the system error probability to remain (almost) unchanged as the size of the profile database grows. To achieve scalability, we propose personalization of verification algorithm. More specifically, for a user $u$ the verification algorithm considers a set of doppelgangers that consists of users that have “close” profiles to $u$ , and uses the set to form additional verification checks for verification requests against $u$ . We use extensive experiments to validate the above, including the increase in the verification error with the increase in the database size, and implement and evaluate our proposed scalable verification algorithm in reducing this error. To our knowledge, this is the first systematic treatment of scalability of BA systems. Our notion of scalability and the approach to achieving it are general and applicable to other distance-based authentication systems.

Few-shot object detection (FSOD) eliminates the dependence on tremendous instances with manual annotations in conventional object detection. We deem that the scarcity of positive samples is the main reason that restricts the performance of FSOD detectors. In this paper, a novel FSOD model via sample processing, namely, FSSP, is proposed to detect objects accurately with only a few annotated samples, which is based on the structural design of the Siamese network and uses YOLOv3-SPP as the baseline. Central to FSSP are our designed self-attention (SAM) and positive-sample augmentation (PSA) modules. The former attempts to better extract the representative features of hard samples, and the latter expands the number and enriches the scale distribution of positive samples, inhibiting the growth of negative samples. For the fine-tuning phase, we modify the classification loss function to increase the punishment for hard samples. Experiments conducted on the PASCAL VOC and MS COCO datasets confirm that the proposed FSSP achieves competitive detection performance compared with state-of-the-art detectors.

Persistent key-value stores have been widely adopted as storage engines for modern IT infrastructures because they provide high performance with simple design principles. Moreover, many key-value stores commonly employ LSM-tree as their index structure due to its attractive features such as high write throughput and storage space efficiency. Unfortunately, LSM-tree has critical drawbacks in that it leads to write/read amplification problem. One of the prevalent solutions for remedying the write amplification problem is the tiering merge policy that reduces the number of rewrites by delaying merge operations. However, in spite of this advantage, the tiering merge policy may lead to a side-effect that induces high read amplification, increasing search/scan cost for upcoming read operations. In this paper, we concentrate on mitigating the high read amplification problem of the tiering merge policy, while maintaining its low write amplification. To achieve this, we propose a novel LSM-tree scheme, called Spatially Fragmented LSM-tree (SFM), which delays merge operations only for the non-read-intensive key-spaces. For this, SFM identifies the read intensity of each key-spaces by dynamically estimating their read/write hotness. We have implemented SFM based on PebblesDB and evaluated the performance benefits of our scheme under real-world workloads of Facebook. Experimental results clearly show that our scheme improves throughput by up to $1.67\times $ compared with the conventional schemes while maintaining low write amplification, and also indicate that its latency is lowered by up to 41.41% on average by mitigating the read amplification problem of the existing schemes, by up to 43.68%.

The detection, classification, and location of grid faults have always been challenging for power engineers. Among these, the detection of the grid fault is the most critical task because, without the detection, the remaining two processes will not start. In this study, a fast light-weight fault detection is proposed. This technique uses an empirical mode decomposition to decompose the fault signals into intrinsic mode functions (IMFs). Effective IMFs were selected using the earth mover’s distance-based selection algorithm. The selected IMFs are then passed through an energy calculator and then through a moving median absolute deviation stage to generate an energy deviation matrix (EDM). Based on the EDM, the occurrence of faults can be determined. The proposed technique was verified by evaluating various grid fault scenarios using the MATLAB/Simulink software platform. Furthermore, the proposed method shows a more precise and satisfactory performance than existing standard fault-detection methods.

In this study, an optimal reactive power (Volt/VAr) control of smart inverters for photovoltaic (PV) and battery energy storage systems (BESSs) to improve the PV hosting capacity (PVHC) of distribution networks is proposed. The primary objective of the proposed method is to improve the PVHC of a distribution network by determining the optimal oversize, dispatch, and control setting of the Volt/VAr functions of the smart inverters for both PVs and BESSs. Concurrently, the optimal locations of the PVs and BESSs are determined. The problem is formulated as a multi-objective mixed-integer nonlinear optimization to maximize the PVHC and minimize the voltage deviation simultaneously. A bio-inspired metaheuristic optimization method, i.e., the slime mould algorithm (SMA), is employed to solve the optimization problem. To assess the efficacy of the proposed PVHC improvement method, extensive simulations are conducted on an IEEE 33-node system using MATLAB software. The simulation results verify that the proposed method improves the PVHC of the distribution network compared to different cases and the default Volt/VAr control settings of the smart inverters. Furthermore, the SMA optimization method provides superior performance in finding the optimal PVHC of a distribution network compared to the conventional metaheuristic optimization methods.

The rapid growth of IoT wireless devices has caused severe spectrum shortage. The Dynamic Spectrum Access (DSA) paradigm is extremely well-suited to alleviate such spectrum shortage. Contrary to legacy spectrum distribution methods, DSA allows for spectrum reuse whenever the primary licensees are idle and not actively utilizing the spectrum. DSA’s database-driven model has been very successful and applicable towards TV white-space frequencies. However, other more fluid spectrum opportunities, such as the National Oceanic and Atmospheric Administration spectrum blocks, continue to be identified and categorized via either (i) solitary or (ii) multinodal cyclostationary and cooperative signal detection/classification methods. The later methods improve significantly the rate of signal detection and classification yet they have severe drawbacks when it comes to preserving the location privacy of users. Moreover, both methods are negatively affected by large delays, partially due to the quiet periods they need to perform while identifying/classifying incumbents. To this end, we extend our previous work by proposing a novel privacy-preserving scheme based on the fusion of DSA and crowd-sensing paradigm. Our scheme provides location privacy for the crowd-sensing users through homomorphic cryptographic constructions. Furthermore, our scheme mitigates fraudulent sensing report attacks by providing robust fraud prevention based on homomorphic cryptographic constructions and reputation algorithms. We provide substantial experiments on real-life datasets which shows that our proposed protocol provides realistic efficiency and security.

In power systems, identification and damping of low-frequency oscillations(LFO) is very crucial to maintain the small signal stability. Hence the computation of eigenvalues, eigenmode shapes, participation factors, and coherency of generators are essential parameters of critical LFO modes. The existing data-driven approaches explore either one or two aspects of modal parameters from the dynamic pattern of the measurement data. In the present work, two approaches i) Iterative Approach(IA) ii) Non-Iterative Subspace(SS) method of data-driven techniques are used to estimate the state-space model of the system under study from the measurement data in a holistic framework. Based on the estimated system model, the eigenvalues of LFO, eigenmode shapes, participation factor, and coherency of associated generators participating in electromechanical oscillations are computed. Finally, from the estimated participation factors for the Inter-area oscillation mode (IAM), the Static Synchronous Compensator (STATCOM) damping controller is designed and placed at the generator with the highest participation factor for damping of inter-area oscillation. The enhancement of damping ratio of inter area mode with STATCOM damping controller is estimated and verified using IA & SS data driven approaches for the first time. In this work, IA uses prediction-error minimization algorithm (PEM) & Parallel computing techniques and SS method uses Multivariable Output Error State Space (MOESP) algorithm for the estimation of Hankel matrix from the measured data. The effectiveness of data-driven approaches are demonstrated by the simulation of a IEEE 4-machine,10-bus power system using MATLAB/Simulink. IA & SS methods incorporating wavelet based denoising techniques are very effective in identifying the LFO modes even with noisy measurement. The efficacy of the denoising to suppress the effect of noise is demonstrated by comparing with noiseless environment. The results of data-driven approaches indicate their high degree of accuracy and efficiency in being consistent with Eigenvalue analysis (EA) performed on the system.

A novel circuit-based mathematical model of an electric arc heater is presented so that an arc heater system can be modeled, and a control algorithm can be developed and simulated. Due to inherent arc nonlinearities and complexities, as well as low amounts of arc heater data, the new model was developed by establishing a holistic approach to implementing the arc as a circuit element, where common circuit analysis and control techniques can be easily applied. The response of the arc heater system was examined at various voltage and current operating points that represent different regions of operation within the arc’s characteristic curve. The simulated data of the arc heater model were compared to the arc characteristics of the experimental data. The experimental data set used for comparison was collected at the Hypersonic Materials Environmental Test System (HyMETS) arc-jet wind tunnel by the NASA Langley Research Center in Hampton, Virginia. Data analysis and simulations were executed utilizing MATLAB and Simulink to compare the newly developed model with the experimental data. The simulations demonstrate a strong correlation between these datasets, indicating the model’s ability to accurately replicate the physical system, while also allowing initial control system development to begin with simplistic proportional-integral-derivative (PID) control of the arc heater.

The authors propose analogical reasoning systems based on first-order predicate logic using deep learning. The proposed systems consist of a model combining recursive neural networks and Word2Vec. When unknown data is input in this trained model, similar outputs to those of the trained data are obtained. Previous studies on symbolic inference using deep learning have focused on deductive and inductive inferences, and they require rule templates that are manually created and provided to the models in advance. However, it is not enough to infer unknown rules from a large amount of data on the Internet. Analogical reasoning is a mode of inference that attempts to solve a new case based on similarity. Thus, it enables inference of rules that were challenging to comprehend in previous studies. As a result of the experiments, unknown rules can be efficiently inferred from known rules contained in the knowledge bases. Using the proposed systems, the authors can extract unknown rules from five of the eight datasets consisting of three types of knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning. Furthermore, the models used in the proposed systems have shown high robustness when using Word2Vec. For this reason, it is suggested that the practical applications of the proposed systems enable efficient inferences from a large amount of data that include noisy data on the Internet.

Human–Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.

In order to evaluate and validate the latest trends of power-hardware-in-loop (PHIL) test setup, the dc-dc buck converter is modelled within a real-time system where the simulation model of the converter is exported to FPGA NI PXIe with a time step of 250 ns. PHIL setup allows high flexibility, the benefit of graphical programming, and advanced investigation of the control system for a converter without any safety concern and with the possibility of testing against situations that rarely occur in the field. The LabVIEW-FPGA has been used as a prototyping environment for a digital controller with the help of OPAL-RT eHS software and NI hardware. Such collaboration enables other software such as MATLAB/Simulink, Multisim, PLECS, PSIM, and LabVIEW Co-simulation for accelerating innovative research and development. This research work presents a more efficient and effectual NI PXIE platform with at least ten times more FPGA capability. This paper highlights the hardware-software toolset’s performance and the proposed methodology by addressing regulation issues in dc-dc converters. For more satisfactory and reliable operation real-time simulation study of a dc-dc buck converter is evaluated at different parametric variations under the closed-loop PI controller. Finally, the executed model’s effectiveness for a closed loop buck converter with real DC loads is validated through the hardware-in-loop (HIL) laboratory setup.