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Supply-chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of Distributed Ledger Technologies (DLT) in supply chain management (SCM) demonstrates the potentials to to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the supply chain, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This paper presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC Security Management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens, they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.

Although incremental sheet forming (ISF) is featured with greater flexibility, lower cost, and shorter leading time, there is less comprehensive sustainability assessment for ISF. In order to summarize the research progress on the sustainability evaluation of ISF and further explore its potential application scenario specifically, the research with respect to energy consumption and environmental impact of ISF are reviewed in this paper. First, the characteristics and research status of ISF including geometry accuracy improvement, forming force prediction, and heating-aided ISF are introduced. Then, study on the influence of processing parameters, equipment, and consumables on energy consumption (both deformation energy and machine tool energy) and forming efficiency are comprehensively reviewed. Current research indicates that neither deformation energy consumption nor electric energy consumption of ISF is lower than stamping process since ISF has long motion trajectory and forming time. In addition, the current methods (such as Life Cycle Assess method) to assess the environmental impact of ISF are discussed. Additionally, the environmental impact of ISF mainly reflects on sheet material, energy consumption, and lubrication oil. Moreover, the comparison of the ISF process and the other processes with regard to sustainability is analyzed, which indicates that ISF has obvious advantage and broad prospects when the cost of molds is considered and the batch size is generally less than 1000. The study provides valuable guidance on the further improvement of ISF towards more efficiency and green manufacturing process.

For the prediction of economic expenses involved in construction industry, cost estimation has become an important aspect of construction management for the prediction of economic expenses and successful completion of the construction work. Cost analysis is crucial and require expertise for accurate and comprehensive estimation. In order to effectively improve the accuracy of construction project cost, this paper establishes an estimation model based on gray BP neural network. It combines the MATLAB toolbox for program design, and learns and tests the input and output of training samples. This article determines the application of grey system theory to optimize the estimation model of Back Propagation (BP) neural network. The viability of the method established in this article, is tested by collecting the engineering cost data in Zhengzhou city and comparing between the standard BP neural network and the gray BP neural network methods. The results show that the average error of the gray system theory optimized BP neural network model designed in this paper is 2.33%. The gray BP neural network model studied in this paper can not only quickly estimate the project cost, but also has high accuracy rate. The outcomes obtained establishes a model with scientific and reasonable construction project cost estimation.

The current onboard wireless and sensors technologies have a significant role in improving day-to-day users’ activities. Examples of these technologies are WiFi, Bluetooth, Cellular, Global Navigation Satellite System (GNSS), proximity, camera, and inertial sensors. Using these technologies provides many applications from the range of communication to the location-based systems (LBS). The LBSs locate people and objects from outdoors into indoors for many purposes, including navigation, entertainment services, and even for security issues. However, the most notable limitations of these technologies include a lack of accuracy and cost-efficiency. Several research types have been attempted to tackle these issues by combining the various technologies, using smart models, and applying optimization algorithms. Therefore, this study reviews many models on the shelf and analyzes the models according to their improvement of accuracy, reduction of time to fix, and cost-efficiency. Specifically, in this study, the optimization algorithms used for positioning purposes are investigated and classified into two groups of algorithms: statistical and non-statistical algorithms. Further, the study also illustrates the weaknesses and limitations of the surveyed algorithms. Finally, the famous challenges and future trends are listed to provide a useful guide for the current readers.

Network Functions Virtualization (NFV) is a network architecture concept to improve network performance. This concept empowers the network capacities and reduces the cost and accelerates the network services and moving them to virtual severs. Mobility Management Entity (MME) is one of the feasible components for virtualization to control the plane due to its non-handling features for user data traffic. However, the MME does not handle the user data and not using any sort of hardware for data processing towards the cloud. The deployment of MME on the cloud is not an appropriate strategy due to the heavy load and complex processes. The main objective of this paper is to develop a suitable software deployment architecture Software-Defined Network-based Network Functions Virtualization (SDN-NFV) that can provide a cloud-optimized design for the MME. The proposed architecture is based on SDN networks to boost and enhance the traditional cellular network processes and functionalities. The proposed architecture contains three tiers including the data plane, control plane, and application plane. The data plane contains the switches, open flow protocol, and OpenSwitch virtual software and responsible to handle the data traffic and forward it to the correct destination based on flow entries in its flow table. The control plane is responsible for the entire network topology and contains the functionalities of traditional non-SDN routers and policies that come from the application plane. The application plane uses a virtualization environment where all the requirements are defined to the corresponding controller. The proposed architecture is evaluated in a simulation where it achieved better performance in terms of latency and throughput.

Screening via low-dose Computer Tomography (CT) has been shown to reduce lung cancer mortality rates by at least 20%. However, the assessment of large numbers of CT scans by radiologists is cost intensive, and potentially produces varying and inconsistent results for differing radiologists (and also for temporally-separated assessments by the same radiologist). To overcome these challenges, computer aided diagnosis systems based on deep learning methods have proved an effective in automatic detection and classification of lung cancer.\ud \ud Latterly, interest has focused on the full utilization of the 3D information in CT scans using 3D-CNNs and related approaches. However, such approaches do not intrinsically correlate size and shape information between slices. In this work, an innovative approach to Multi-view Convolutional Recurrent Neural Networks (MV-CRecNet) is proposed that exploits shape, size and cross-slice variations while learning to identify lung cancer nodules from CT scans. The multiple-views that are passed to the model ensure better generalization and the learning of robust features.\ud \ud We evaluate the proposed MV-CRecNet model on the reference Lung Image Database Consortium and Image Database Resource Initiative and Early Lung Cancer Action Program datasets; six evaluation metrics are applied to eleven comparison models for testing. Results demonstrate that proposed methodology outperforms all of the models against all of the evaluation metrics.

This article presents an efficient fingerprint identification system that implements an initial classification for search-space reduction followed by minutiae neighbor-based feature encoding and matching. The current state-of-the-art fingerprint classification methods use a deep convolutional neural network (DCNN) to assign confidence for the classification prediction, and based on this prediction, the input fingerprint is matched with only the subset of the database that belongs to the predicted class. It can be observed for the DCNNs that as the architectures deepen, the farthest layers of the network learn more abstract information from the input images that result in higher prediction accuracies. However, the downside is that the DCNNs are data hungry and require lots of annotated (labeled) data to learn generalized network parameters for deeper layers. In this article, a shallow multifeature view CNN (SMV-CNN) fingerprint classifier is proposed that extracts: 1) fine-grained features from the input image and 2) abstract features from explicitly derived representations obtained from the input image. The multifeature views are fed to a fully connected neural network (NN) to compute a global classification prediction. The classification results show that the SMV-CNN demonstrated an improvement of 2.8% when compared to baseline CNN consisting of a single grayscale view on an open-source database. Moreover, in comparison with the state-of-the-art residual network (ResNet-50) image classification model, the proposed method performs comparably while being less complex and more efficient during training. The result of classification-based fingerprint identification has shown that the search space is reduced by over 50% without degradation of identification accuracies.

Innovative cloud computing services based on Heterogeneous Cellular Mesh Networks (HCMN) offered adaptive data storage functions and a flexible environment. Channel Assignment is one of the significant tasks in these networks by using Partially Overlapped Channels (POCs) in Multi-Radio Multi-Channel HCMNs. POCs can help to maximize the network throughput, but the availability of a fewer number of Non-Overlapping Channels makes these processes challenging. Mostly 802.11 Industrial, Scientific, and Medical radio bands are used in commercial and private deployments. The use of the same frequency band in neighboring networks is the leading source of external interference. As interference is a major cause of performance degradation in networks and needs to minimize it, but interference from co-located networks is overlooked in existing POC assignments. In this paper, a centralized Interference Aware Partially Overlapped Channel Assignment is proposed that takes both external and internal interference into account along with the degree of overlap among adjacent channels. Initially, it binds interfaces of each node to its one-hop neighbors and identifies all possible links by constructing a neighborhood graph. Average external interference is measured in terms of channel utilization and the number of interfering radios at each channel. The proposed scheme selects and assigns a channel with minimum total network interference for a link. Simulation results indicated a considerable improvement in terms of throughput, packet loss ratio, and end-to-end delay as compares to the existing scheme.

In this work, the idea of using shift-invariant discrete wavelet transform and sparse fusion-based magnetic resonance and computed tomography image fusion technique is presented. Source images from different modalities are split into different scale components along with high-level components using shift-invariant discrete wavelet transform. Approximation components are fused using sparse fusion. Different computed weights are joined to be used with source images to get the required fused image as output. Metrics-based visual and quantitative results clearly indicate the worth of proposed new approach in comparison with other existing fusion strategies.

Nodes in a body area network (BAN) are miniature wearable or implantable battery-powered wireless sensors which continuously transmit real-time vital physiological data of a patient to remote health-care center while remaining in close proximity to the human body. Therefore, BAN nodes should have the features of high data rates and low transmit powers to protect the human body, environment and bio-medical equipment from harmful exposure of electromagnetic radiations and electromagnetic interference (EMI). Ultra-wideband (UWB) signals have low allowable transmission power and high data rates. Therefore, we propose a low cost, low powered and secure optical body area network (OBAN) composed of four UWB-BAN nodes each transmitting at a data rate of 30 Mbps. At the control node, UWB signals from UWB-BAN nodes are encoded using spectral amplitude coding-optical code division multiple access (SAC-OCDMA) scheme and the combined signal is transmitted over free space optics (FSO) channel towards remote health-care center. At the health-care center, the combined signal is decoded and UWB signal of each UWB-BAN node is photo-detected for analysis of patient’s data. Log-normal channel model is considered between control node and the health-care center. The signal received from each UWB-BAN after propagation through the FSO channel is analyzed through Bit error rate (BER) results. Similarly, cost efficiency of the proposed architecture is also evaluated through a detailed cost analysis. It was observed that the proposed architecture requires the UWB-BAN nodes to have low receiver sensitivities with the added benefits of cost-efficiency and data security.

Multi-focus image fusion aims to obtain necessary details from multiple source images, having varied level of focus depths, in order to generate an all-in-focus fused image that ideally contains all the information from source images. This paper presented an image matting-based fusion approach to combine focus information of multiple source images based on correlation between nearby pixels. First the focused pixels of source images are identified by perceiving the sharpness of the images by utilizing multiple sharpness metrics. Trimap is generated from focused maps to obtain prior information and image matting is applied to accurately segment the focused and de-focused regions of the source images. In the end, the focused regions from multiple source images are integrated to obtain a well formed and consistent fused image. Experiments and comparison with various existing fusion techniques verify the significance of proposed technique.

Internet of Things (IoT) is one of the heterogeneous network consists of vast number of connected devices. These networks need smart architecture to accommodate the expected increase of data generation. Software-Defined Networks (SDN) have gained popularity as a key enabler for automated provisioning and ease in IoT network resources management. For ubiquitous coverage, these network involve seamless handoff of the user devices ensuring the provision the services as per user demand. Although the basic inter-domain handoff rules exist in the next SDN domain are not sufficient to provide the primary domain services to the IoT networks. This issue leads to degradation of post-handover performance. In this paper, the SDN based Heterogeneous Inter-Domain Handoff system for IoT (SDN-HIIoT) is proposed that enables SDN-Controller to exchange flow rules by using the East/West application programming interface. The proposed system is using a process of hybrid flow rules execution on heterogeneous SDN domain for provision of Quality of Services (QoS) to mobile users the same as the primary domain. Experiment results are provided to demonstrate the proposed system can significantly improve the post-handoff performance and provides ubiquitous services.

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives. Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19. However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered. In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.

Optical signal regenerators are required at regular intervals in a long-haul optical link to re-amplify, re-shape and in some cases re-time the signal that has been distorted by the impairments of the link. A major benefit of all-optical regenerators is that they do not require the optical signal to be converted into the electrical domain for performing the above-mentioned tasks. This study analyzes the performance of our previously proposed optical signal regenerator along with two other regenerators that are based upon cross-phase modulation (XPM) and four wave mixing (FWM), respectively. The aim of the study is to determine the most suitable regenerator for a specific optical link based on its characteristics. The characteristics of the regenerators that are analyzed in this study include capability to reject noise, extinction ratio, reduction in timing jitter, chirp induced over the regenerated signal and energy yield of the regenerator. The analysis shows that each of the three regenerators have different capabilities that make them suitable for different optical links.

In the Internet of Vehicles (IoV), vehicles communicate wirelessly with other vehicles, sensors, pedestrians, and roadside units. IoV is aimed at improving road safety, driving comfort, and traffic efficiency. However, IoV is exposed to a range of threats to security and privacy. The presence of dishonest and misbehaving peers in the system is of a major concern, which may put lives in danger. Thus, establishing trust among these probable untrusted vehicles is one of the most significant challenges of such a network. The critical pitfalls of existing and traditional mechanisms are scalability, a single point of failure, maintaining the quality of service, verification, and revocation and dealing with sparsity, consistency, availability, efficiency, robustness, privacy concerns are some of the biggest challenges to be addressed. Blockchain technology, with its great success in applications like cryptocurrencies and smart contracts, is considered as one of the potential candidates to build trust in IoV. In this paper, we propose a blockchain-based decentralized trust management scheme using smart contracts. Specifically, we introduce the concept of blockchain sharding for reducing the load on the main blockchain and increasing the transaction throughput. Our proposal has two key contributions: blockchain to maintain and update reliable and consistent trust values across the network and incentive scheme to encourage peers to perform well. We also conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms in the real world.

High embedding capacity is considered as one of the significant challenges in information hiding. In the spatial domain, most of the high capacity based strategies employed the least significant bit (LSB) and pixel value differencing (PVD) techniques. However, these are suffered from the incorrect recovery of secret data, inefficient utilization of pixel intensities, and limited usage of pixel difference range table. In this paper, a new solution is proposed to alleviate the shortcoming by exploiting the pixel intensities ranges for high embedding rates while achieving the 100% recovery of secret data in the extraction phase. The proposed method divides the pixels into non-overlapping pixels block; further, compute the pixels differences along with respective ranges to determine the size of secret bits for the underlined adaptive LSB embedding process. In the extensive experiments, the proposed scheme outperforms the embedding capacity and maintains acceptable visual imperceptibility at various embedding rates, and also shows successfully the resistance against RS analysis and machine learning-based detection attacks.

Multi-spectralimages suffers from out-of-focusblur due to well focused camera at the reference imaging channel. A framework for out-of-focus images de-blurring using texture extraction and modified Fourier transform is proposed. The texture is extracted from the blurred image using region covariance. Fourier transform is modified by modification of guided image (as prior) using L0 gradient projection followed by detail amplification which is used as an input for Fourier transform. For further enhancement, the multi-spectral de-blurred images are smooth, and ringing artifacts are minimized by combining with the textures extracted to ensure maximum edge preservation. Comparison of proposed and existing schemes on different multi-spectral images explains the advantage of the proposed scheme for de-blurring in terms of edge preservation, noise and artifacts removal.

Rapidly changing market has accentuated the need for developing robust and responsive schedules. Intelligent heuristics techniques have been widely used to provide scheduling alternatives for resource constrained scheduling and identify critical activities essential for monitoring and controlling these processes. We argue that traditional parameters based solely on slack calculation are insufficient to characterize tasks as critical and instead define a fuzzy parameter-based approach, which relies on process and task-specific information. Proposed parameter is used to rank activities on a scale based on their criticality to develop a heuristic. It was observed that the proposed intelligent heuristic performed better on well-known benchmark datasets in terms of both lead times and computation time. An improvement of 1.5–2.5 percent was observed in traditional performance measure of order lead times while for non-traditional criterion measuring robustness, improvement of 30–35 percent was observed. Furthermore, current research provides meaningful insight on critical tasks at the shop-floor.

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.

Asynchronous systems are native to a full custom domain. Their implementation using auto place-and-route tools requires dynamic calibration of interconnects delays in addition to the placement of predefined static delay elements. This paper presents a completion detector for a single-rail bit encoded datapath that, as an adaptive-delay element, eliminates the need to insert any predefined delay element and caters to routing delays dynamically. A programmable pulse-generator is also proposed that empowers the designers to generate clock signals based on the timing report obtained from the CAD tool to drive various synchronous subsystems and embedded resources like BRAMs in FPGAs. Employing these components, we present an asynchronous pipeline model with implicit control to expedite migration from the traditional synchronous pipelines to their asynchronous counterparts. A single-rail bit encoded datapath has been used to utilize chip area effectively instead of a delay-insensitive dual-rail datapath, and a two-phase handshake protocol has been adopted as opposed to a four-phase handshake protocol to lower handshaking overhead. A RISC processor validates the proposed asynchronous pipeline model, exhibiting a smooth functionality and power-delay parameter comparable to that of a synchronous pipeline, in addition to ease of routing and avoiding clock skews in a complex system-on-chip.

An effective wavelet based scheme coupled with finite difference is used for the solution of two nonlinear time dependent problems namely: Burgers' and Boussinesq equations. These equations have wide-spread application in many fields such as viscous medium, turbulence , uid dynamics, infiltration phenomena etc. The proposed scheme convert the partial differential equations (PDE) to system of algebraic equations. The obtained system can be solved easily. In this paper convergence of the scheme is also discussed to show validity of the technique. Effectiveness of the scheme is shown with the help of test problems. Numerical results verify that the suggested scheme is more accurate, convenient, fast and require low computational cost.

The paper presents an iterative matting technique for extraction of underwater objects from images. The technique adopts histogram division and stretching to obtain multiple images of different contrast levels that exhibit all image details. For each contrast image, alpha matte is produced and is further refined with every iteration. In the end, fuzzy weights are assigned to the alpha mattes obtained at different contrast levels that are combined using weighted average. The resultant alpha matte thus includes more accurate pixels from multiple alpha mattes and generates much refined matte image. The proposed technique is tested, visually and quantitatively, on a manual dataset containing 50 images. The less MSE shows that the proposed technique achieves noticeably higher accuracy as compared with contemporary image matting techniques.

Mm-wave MIMO communication makes a hybrid combination of analog RF and digital baseband processing more attractive, where digital baseband precoders/combiners able to adapt to the pre-defined analog (switch based) RF processors. Non-uniform two-dimensional quantized azimuth and elevation angle grid antenna array responses are suggested for uniform planar array (UPA) and are proven orthogonal. Training vectors (or sensing matrix) are designed for suggested antenna array response with unitary RF processing for UPA in mm-wave hybrid MIMO system. Proposed training vectors achieve minimized total coherence of the equivalent sensing matrix for hybrid MIMO system. Open-loop channel estimation of the mm-wave channel is done by using iterative re-weight based super resolution algorithm to exploit its sparse nature. Extensive simulations reveal the benefit of coherence optimization where normalized mean squared error is reduced and spectral efficiency is improved in comparison to existing methods.

A cost efficient and reliable single mode fiber and free space optics based hybrid 32 × 5 Gbps ultra dense wavelength division multiplexed-passive optical network architecture employing optical comb and polarization multiplexing is proposed. The proposed architecture is a step forward towards shifting the next generation wireless network traffic over existing passive optical network to achieve the optimum bandwidth, capacity, quality of service and cost efficiency for mobile subscribers. Self-restorable passive optical networks having fiber based feeder and distribution paths are widely researched in various studies in the past. However, to the best of our knowledge, a self-restorable passive optical network architecture employing free space optics based protection path has not been proposed so-far. The effect of varying polarization mode dispersion from 0.02 to 0.06 ps . km - 0.5 and the refractive index structure parameter of the Gamma-Gamma channel model from 5 × 10 - 16 to 5 × 10 - 12 m - 2 / 3 is evaluated. A techno-economic performance analysis of the architecture for downstream is performed by considering both the distribution and protection paths. It has been shown that the receiver sensitivity for a polarization mode dispersion of 0.06 ps . km - 0.5 is around - 22 dBm and for strong turbulence is around - 20 dBm .

Due to the constantly changing sea surface, there is a high risk of link fragility caused by sea waves when different marine users are intended to establish stable links for communication. To ensure stability with less delay, finding a stable route is one of the crucial aspects of maritime networks. In order to achieve this aim, we propose a routing protocol for cognitive maritime networks based on software-defined networking (SDN). This SDN-based cognitive routing protocol provides stable routes among different marine users. To provide the global view of the whole network, a main controller is placed close to the seashore, whereas the localized views are provided by the cluster heads. Autonomous surface vehicles are used as gateways under sparse network conditions to collect and transport data among clusters, and to and from the main controller. This is an SDN-based ship-to-ship communication scheme where two ships can only establish a link when they not only have consensus about a common idle channel but are also within the communication range of each other. We perform extensive simulations to test the proposed scheme with different parameters and find better performance in comparison with both SDN-based and non-SDN-based schemes in terms of end-to-end delay, packet delivery ratio, and routing overhead ratio.

The blur of image is displayed by convolving an image with the blur kernel. Thus, estimating blur kernel is significants of image de-blurring. We aim at obtaining optimized blur kernel of image for de-blurring. Kernel estimation and optimization for de-blurring of image is proposed in this paper. Mask is created for kernel estimation through super pixels and gradient map (generated through illuminant layer). Structural information is extracted through creation of mask through super-pixels, instead of using exemplars and together with the illuminant part of image and gradient map estimates the kernel which is optimized using super-resolution. The proposed method extracts good structural information and edges, hence better de-blurring as compared to state-of-art de-blurring methods.

Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems. MVA algorithm inspires the creation of the next population to be very close to the solution of initial population, which mimics the nature of parallel worlds in multiverse theory. Additionally, MVA distributes the solutions in the feasible region similarly to the nature of big bangs. To illustrate the effectiveness of the proposed algorithm, a set of test problems is implemented and measured in terms of feasibility, efficiency of their solutions and the number of iterations taken in finding the optimum solution. Numerical results obtained from extensive simulations have shown that the proposed algorithm outperforms the state-of-the-art approaches while solving the optimization problems with large feasible regions.

Palmprint‐based human authentication has shown great potential for civil, forensic, and corporate security applications in recent years. Palmprint recognition systems suffer because of large palmprint sizes and the presence of a large number of creases and erroneous minutiae that make the enhancement and matching phases a challenge. In this study, a novel approach is presented based on efficient enhancement and a two‐stage matching technique that demonstrates highly accurate identification results. The enhancement approach extracts minutia features from high‐quality regions based on local ridge characteristics. The selected minutiae are then matched using a two‐stage local and global minutiae neighbour‐based matching technique. To demonstrate the performance of the proposed technique, comparisons with open‐source algorithms are made based on equal error rate and detection error trade‐off graph. The results confirm the efficacy of proposed palmprint enhancement and identification technique.

Fingerprint-based human authentication has shown great potential for civil, forensic and corporate security applications in recent years. For large-scale databases, the complexity of the identification system increases and implementing these systems on general-purpose sequential computing devices becomes challenging. Field Programmable Gate Array (FPGA) has demonstrated to be an efficient tool for the acceleration of computationally challenging applications by utilizing parallelism in the computations. In this study, an FPGA-based hardware accelerator is exploited to propose a fast and robust fingerprint identification solution that is based on a generalized minutiae neighbor based encoding and matching algorithm. The proposed FPGA implementation employs the Distributed RAM resources efficiently by using them as look-up tables for matching the encoded minutiae features. The proposed FPGA-based fingerprint matching system has the potential to match 2.75 million fingerprints per second while maintaining a low error rate. The proposed system can be deemed as an effective solution for Automated Fingerprint Identification Systems (AFIS) for large-scale applications.

In this paper, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. Novelty of this paper lies in (i) the construction of the proposed ETNAC schemes, (ii) the design of event-triggering conditions, and (iii) the design of an observer called the modified state observer (MSO). In the proposed schemes, the MSO, the controller, and the event-triggering mechanisms are constructed and organized in a way such that they provide the control system designer with flexibility to choose between the one-way or two-way data exchange and also between the dynamic or static triggering conditions. The event-triggering conditions are designed on the basis of real performance parameters, such as the estimation/tracking errors that render control updates more on actual system events instead of the often-used extended time sampling. Another unique feature of ETNAC is its online uncertainty approximation capability even during inter-event times, which makes the controller robust and efficient. This part is developed with the help of an artificial neural network (ANN) and a polynomial regression-based MSO. The MSO formulations have two tunable gains, which allow fast uncertainty estimation without inducing high frequency oscillations, even while the system is in a transient state. Lyapunov analysis is used to show the stability of the system as well as to develop the event-triggering conditions. Effectiveness of the proposed controllers is demonstrated using benchmark numerical examples.

Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named volcano eruption algorithm is proposed in this paper, which is inspired from the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-the-art literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem. Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of vehicles problems efficiently.

In this paper, fingerprint referencing methods based on wireless fidelity Wi-Fi received signal strength (RSS) have used for indoor positioning. More precisely, Naïve Bayes, decision tree (DT), and support vector machine (SVM) one-to-one multi-classes and error-correcting-output-codes classifier are to enable accurate indoor positioning. Then, normalization is used to reduce positioning error by reducing the fluctuation and diverse distribution of the RSS values. Different devices are used in this experiment; the training dataset is not included in the main dataset. Nonetheless, the learned model by the SVM algorithm cannot be affected by the elimination of train datasets of the test device. The efficiency of DT is lower than the other machine learning algorithms, because it performs by Boolean function, and it provides the low accuracy of prediction for dataset than the algorithms. Naïve Bayes technique based on Bayes Theorem is better than DT and close to SVM for positioning approves that 1–1.5 m positioning accuracy for indoor environments can be achieved by the proposed approach which is an excellent result than traditional protocol.

The exponential growth in the use of Internet of Things (IoT) devices has introduced numerous challenges, in particular dealing with new security threats. In addition, for connecting heterogeneous devices using different protocols, large networks need resilient software-based security systems that can defend against unprecedented attacks for which the traditional security countermeasures prove to be ineffective. Furthermore, to deal with the ever growing onslaught on data and networks, modern security systems need to utilize novel machine learning mechanisms. This paper proposes a software-based architecture that provides network function virtualization (NFV) capability to combat malware spread for heterogeneous IoT networks. To build a scalable and generalized Intrusion Detection System (IDS), we propose for these networks a RNN-LSTM learning model that can predict malware attacks in a timely manner for the NFV to deploy appropriate countermeasures. In addition, we investigate the scalability of the network and discuss how the generalized IDS can deal with a broad range of malwares that can be detected. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) epidemic model to moniter the spread of the malware attack and subsequently provides patching to the system. Our analysis focuses primarily on the feasibility and the performance evaluation of the proposed integrated RNN-LSTM and NFV architecture.

This study presents a technique to automatically extract the aircraft from video sequences. The technique incorporates object tracking algorithm to separate the region of interest (ROI), i.e. movement of aircraft, from video sequence, to reduce the computational complexity. Trimaps are generated automatically for given ROI using non-rigid image registration to obtain the prior information of the object and alpha mattes are estimated for each frame using KNN matting algorithm. However, the alpha matte of static region is estimated only once, that is combined with the alpha matte of moving region to generate final alpha matte. Simulations performed on different videos show that the proposed technique not only reduces the computational complexity, but also gives competitive results by generating high quality alpha maps.

With the explosive growth of network security threats, there is a dire need to build secure network systems. In this article, we address the challenges of modeling and detecting advanced network attacks. In particular, we investigate how interleaving multiple multi-stage can exacerbate the stealthiness of the attack and deceive network intrusion detection systems. We design a detection architecture based on a leading statistical machine learning technique, HMM. The proposed architecture deploys a set of HMM templates of recognized multi-stage attacks to detect and track the progress of stealthy attacks. Extensive simulation experiments are conducted to assess the performance of the proposed architecture for multiple multi-stage attack scenarios in the presence of imperfect partitioning of network data streams and false alerts with various rates.

Image recoloring using wavelet transform, in-painting and dichromatic reflection model is proposed. Detailed wavelet bands are passed through a sharpening filter whereas the approximation band is p...

A matting technique to extract the targets from synthetic aperture radar (SAR) images is presented. Binary segmentation is performed initially for rough identification of target boundaries. Trimap is then estimated by combining the boundary structures of the input and segmented images using guided filter. In order to improve the accuracy of estimated trimap, super-pixels based segmentation is performed. A propagation based matting algorithm is then applied to separate the target from non-target region. Simulations conducted on different SAR images from MSTAR database show significance of proposed technique.

Radio access technologies, such as C-V2X and DSRC, have been used to support robust communication in connected vehicles scenarios. However, existing studies mostly dealt with homogenous vehicular networks, where coexistence of different radio access technologies in vehicles are not considered. More precisely, such multi-radio interface environments burden communications among vehicles. In this article, we first review DSRC and C-V2X radio access technologies and existing packet relaying mechanisms that are specifically designed for homogenous or heterogeneous vehicular environments. We then present a quality of service aware relaying algorithm (QR) that incorporates multi-metric to prioritize dual interface vehicles (DVs) and provides robust communications among vehicles that are equipped with different radio access technologies. The simulation results confirm the superiority of the proposed QR in terms of message reception and relaying count as compared to the standard protocols.

In this paper a method is proposed to enhance the saliency of selected region with respect to its surrounding to improve user’s attention. Bilateral filter is first used to make the ROI smoother to reduce the unwanted details in the defocus map. Hue component of ROI is adjusted and the outline is extracted using neighbouring data. The outline in resultant image is modified using hue component to direct viewer’s attention in a certain region. The proposed method is applied on different natural images and the results are compared with the state-of-the-art existing methods to prove the proposed method’s effectiveness.

This work presents a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing (HPC). The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive method for acquiring hemodynamic signals from the brain with advantages of portability, affordability, low susceptibility to noise, and moderate temporal resolution that serves as a plausible solution to real-time imaging. fNIRS is an emerging brain imaging technique that measures brain activity by means of near-infrared light of 600–1000 nm wavelengths. Recently, there has been a surge of studies with fNIRS for the acquisition, decoding, and regulation of hemodynamic signals to investigate their behavioral consequences for the implementation of brain–machine interfaces (BMI). In this review, first, the existing methods of fNIRS signal processing for decoding brain commands for BMI purposes are reviewed. Second, recent developments, applications, and challenges faced by fNIRS-based BMIs are outlined. Third, current trends in fNIRS in combination with other imaging modalities are summarized. Finally, we propose a feedback control concept for the human brain, in which fNIRS, electroencephalography, and functional magnetic resonance imaging are considered sensors and stimulation techniques are considered actuators in brain therapy.

Emerging cyberphysical systems incorporate systems of systems that have functional interdependencies. With the increase in complexity of cyberphysical systems, the attack surface also expands, making cyberphysical systems more vulnerable to cyberattacks. We present a novel security architecture that localizes the cyberattack in a timely manner and simultaneously recovers the affected cyberphysical system functionality.

The main drawback of pioneer frequency limited Gramians-based technique by Wang and Zilouchain for discrete time system is that it yields unstable reduced order models (ROMs) of stable original system. There exist some techniques in literature that produce stable ROMs, however, these techniques yield relatively large approximation error. In this brief, three modified techniques of model reduction for discrete time systems are being presented. These techniques preserve the stability in ROMs, mostly yield low approximation error (when compared to existing stability preserving techniques) and additionally contain frequency response error bounds. Several related examples are also illustrated. Comparison of results show the effectiveness of proposed techniques.

It is often difficult and essential to distinguish between focused and de-focused structures in an image. To properly handle such structures, an image fusion technique is developed for multifocus images using high level discrete wavelet components and guided filter. The source images are decomposed using wavelet transform and high level components are processed using gradient magnitude and guided filters to obtain fusion weights to refine the fusion process. Variety of images obtained from standard datasets are used in the simulations to test performance of proposed technique. The fused image obtained using proposed technique outperforms visually and quantitatively as compared to existing techniques.

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty., Bayesian-based convolutional neural network (B-CNN) for tuberculosis identification: Input chest x-ray is given to N number of CNNs. Dropout is enabled at testing stage to get posterior probability distribution (PPD) of classes.

This study presents a shallow and robust road segmentation model. The computer-aided real-time applications, like driver assistance, require real-time and accurate processing. Current studies use Deep Convolutional Neural Networks (DCNN) for road segmentation. However, DCNN requires high computational power and lots of labeled data to learn abstract features for deeper layers. The deeper the layer is, the more abstract information it tends to learn. Moreover, the prediction time of the DCNN network is an important aspect of autonomous vehicles. To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations of the input image. Gradient information of the input image is used as additional channels to enhance the learning process of the proposed deep learning architecture. The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions. The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as compared to baseline CNN consisting of only RGB inputs. Furthermore, the comparison of the proposed method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme performs better while being more efficient during training and evaluation. Unlike traditional segmentation techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only the encoder network. The proposed MVS-CNN has been trained and validated with two well-known datasets: the KITTI Vision Benchmark and the Cityscapes dataset. The results have been compared with the state-of-the-art deep learning architectures. The proposed MVS-CNN outperforms and shows supremacy in terms of model accuracy, processing time, and segmentation accuracy. Based on the experimental results, the proposed architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.