Real-time Scenarios Research Articles

Novel Cross Optimization based Trusted and Opportunistic Routing Scheme in Wireless Sensor Network

Opportunistic routing in wireless networks under mobile computing domain is one of the point of attraction for the research. Consistency along with routing security are the challenges faced by many old schemes which results performance to be compromised. However the existing techniques developed for effective routing are cost effective without the concern for the exact positioning of nodes in the network but while designing the scheme, security still requires attention. A novel cross optimization based trusted and opportunistic routing scheme for WSN is proposed in this paper which is referred as Unified Trusted and Optimized Routing (UTOR) scheme. The prime intention of UTOR is to realize optimized data movement along with routing and data security. UTOR functions in two stages, the first stage recognizes and unites the unified nodes depending on defined liberal constant(LC) which has definite trust, link stability, and quality components. Definite trust as well as link stability can be measured directly but time to live and the associated delay are considered for measuring the quality. Whereas in second stage optimized nodes selection takes place with the help of proposed UTOR over defined proclivity function(PF) based on definite_trust(DT), link_stability (LS), quality_of_node(QN) and distance. Proposed UTOR’s performance is evaluated based on performance measures for Ad-Hoc sensor network of varying range of dynamic nodes in the presence of black_hole and DoS attacks. UTOR exposes relatively superior throughput and detection rate at the same time showcases minimal distance and delay, which are comparatively better than competing schemes. Significantly higher throughput and detection_rate as 44.1 and 55.7 respectively alongside low distance and delay as 168.2 and 13 are shown by UTOR which are comparatively better performance parameters than competing schemes. UTOR’s effective use in WSN may be under real-time scenarios such as environmental monitoring, smart farming, automation industries etc.

Empirical Analysis of Electron Beam Lithography Optimization Models from a Pragmatic Perspective

Electron Beam (EB) lithography is a process of focussing electron beams on silicon wafers to design different integrated circuits (ICs). It uses an electron gun, a blanking electrode, multiple electron lenses, a deflection electrode, and control circuits for each of these components. But the lithography process causes critical dimension overshoots, which reduces quality of the underlying ICs. This is caused due to increase in beam currents, frequent electron flashes, and reducing re-exposure of chip areas. Thus, to overcome these issues, researchers have proposed a wide variety of optimization models, each of which vary in terms of their qualitative & quantitative performance. These models also vary in terms of their internal operating characteristics, which causes ambiguity in identification of optimum models for application-specific use cases. To reduce this ambiguity, a discussion about application-specific nuances, functional advantages, deployment-specific limitations, and contextual future research scopes is discussed in this text. Based on this discussion, it was observed that bioinspired models outperform linear modelling techniques, which makes them highly useful for real-time deployments. These models aim at stochastically evaluation of optimum electron beam configurations, which improves wafer’s quality & speed of imprinting when compared with other models. To further facilitate selection of these models, this text compares them in terms of their accuracy, throughput, critical dimensions, deployment cost & computational complexity metrics. Based on this discussion, researchers will be able to identify optimum models for their performance-specific use cases. This text also proposes evaluation of a novel EB Lithography Optimization Metric (EBLOM), which combines multiple performance parameters for estimation of true model performance under real-time scenarios. Based on this metric, researchers will be able to identify models that can perform optimally with higher performance under performance-specific constraints.

Optimizing Hybrid Microgrids in Real-time: A Comparative Analysis of Two Reinforcement Learning Training Methods

Reinforcement learning has been employed in recent research articles to optimize the energy storage system scheduling in microgrids, aiming to reduce overall system costs. However, applying reinforcement learning in real-time scenarios introduces uncertainties and delays due to the extensive training required to develop the optimal policy for the storage system. This work addresses these challenges and explores potential solutions for real-time dispatch control actions of the battery in a grid-tied microgrid. The study considers different approaches for training the agent, distinguishing between online and offline scheduling of the energy storage system. The limitations of these approaches and their implications on real-time performance are also analyzed. By developing a comprehensive microgrid model and comparing two training approaches, this research contributes to novel insights for efficient real-time scheduling of energy storage systems in grid-tied microgrids. The proposed approach presents a promising path towards addressing uncertainties and achieving optimal operation in grid-tied microgrids. In terms of average cost per year, the difference between the two approaches is 4% if foresight of the real data is perfect, otherwise the real-time approach is more cost-effective.

Toward an Educative EEG-Based neuroIIR System for Adapting Contents

The pursuit of information is an integral aspect of a learner’s daily routine, often facilitated by information retrieval systems. In this study, we explore the correlation between text complexity and reading comprehension through the lens of neuroinformatics. Our objective is to ascertain whether it is feasible to infer the difficulty level of a text for a learner based on their brain activity, quantified by an electroencephalogram (EEG), during the information retrieval process. To accomplish this, we administered a section of the Woodcock Reading Mastery Test to our participants, evaluating their average reading fluency. Subsequently, the 18 participants under observation read paragraphs of varying complexity and responded to questions pertaining to the text. Utilizing the amassed data, we trained a deep learning model known as EEGNet to autonomously discern the complexity of the text being read, relying on the EEG signals. Furthermore, we conducted a comparison with previous research that employed a distinct characterization approach and employed a random forest algorithm for classification. The outcomes revealed that the present approach (EEGNet) outperformed the former method, achieving an accuracy rate of 80.83% across all subjects, whereas the random forest algorithm achieved a mere 50.28% accuracy. Our findings suggest the potential to classify the EEG signals in accordance with the level of difficulty a student encounters when comprehending the text. We propose several avenues for extending our work into real-time scenarios.

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

Unveiling out-of-distribution data for reliable structural damage assessment in earthquake emergency situations

To ensure accurate and effective emergency responses following an earthquake, one must promptly and accurately assess damage to structures with minimal manual effort. An advanced approach for evaluating structural damage is to use machine learning techniques for automated tagging or damage classification. However, most previous studies evaluated models using simple and unrealistic datasets limited to in-distribution (ID) datasets for training and testing. Although this approach may yield satisfactory results within the confines of the training dataset, it is not justifiable for real-time scenarios, in which the testing dataset may differ. Hence, a novel methodology is proposed herein that focuses on the detection of out-of-distribution (OOD) data. By subjecting a network to outliers, the model can effectively identify data outside the domain of the training dataset. A custom loss function is adopted, where both the cross-entropy loss from the ID training dataset and the log loss from the outlier dataset are incorporated. The effectiveness of this approach is demonstrated through its application in the post-event rapid damage assessment of bridges and shear walls subjected to seismic loading. A single network is employed to classify ID data into their respective classes, whereas the OOD data are shown to belong to the OOD class. The results highlight the significant accuracy achieved in the simultaneous prediction of ID and OOD data. By incorporating the detection of OOD data, this study enriches the methods for improving the reliability and accuracy of structural damage assessments in earthquake emergency responses, thus enabling more informed decision-making.

Improved Pest-YOLO: Real-time pest detection based on efficient channel attention mechanism and transformer encoder

Insect infestations and pests inflict significant losses in agriculture, substantially augmenting the demand for automated pest detection and early pest management in the cultivation process. However, the task of multi-class pest identification, involving both localization and classification, is exceptionally challenging due to the small size, great similarity, and environmental variability of pests. This paper presents an enhanced version of our previous work, Pest-YOLO, aimed at improving accuracy while maintaining real-time pest detection. The improved Pest-YOLO incorporates two key advancements: an efficient channel attention (ECA) mechanism for improved feature extraction and a transformer encoder for capturing global features. We replace the original squeeze-excitation attention mechanism with the ECA mechanism, effectively improving the model's ability to extract essential features from pest images. Additionally, we introduce the transformer encoder to the convolutional neural network (CNN) architecture to enhance its capability to capture global contextual information. To further enhance the expressiveness of features for small targets like agricultural pests, we propose a feature fusion method called cross-stage feature fusion (CSFF). This method significantly improves the representation of small targets during the feature fusion stage. Through experiments on the Pest24 dataset, our method achieves an impressive mean average precision of 73.4%, surpassing the performance of state-of-the-art methods. These results demonstrate the effectiveness of our improved Pest-YOLO model in accurately detecting pests in real-time scenarios.

Modeling challenges for Industry 4.0 implementation in new energy systems towards carbon neutrality: Implications for impact assessment policy and practice in emerging economies

New energy systems (NESs) in a circular economy (CE) perspective may contribute significantly to achieving emerging economies’ carbon peaking and neutrality goals. However, CE-based NESs in developing countries towards carbon peaking and neutrality remain unexplored in scientific research and development. Additionally, challenges for I4.0 implementation in the CE context for NESs are still under research gaps in emerging economies. The previous research mainly focused on qualitative discussions on carbon neutrality, I4.0 challenges, and opportunities for renewable energy generation from waste biomass. Therefore, this scientific work, for the first time, offered an innovative decision support model integrating qualitative management tool OGSM (Objective, Goals, Strategies, and Measures), Dombi t-norm and Dombi t-conorm operator based Interval-valued Fermatean fuzzy number (IVFFN) Delphi and IVFFN-Decision Making Trial and Evaluation Laboratory (DEMATE) as a new extension of DEMATEL for assessing interactions among I4.0 implementing challenges in a CE context for NESs towards carbon neutrality which can handle border scale of uncertainty and ambiguity. The findings indicated that out of fifteen I4.0 challenges, nine were identified as causal challenges and the rest as effect group challenges, suggesting that improving causal challenges can notably enhance the effect group challenges. “Lack of aptitude to integrate sophisticated I4.0 infrastructure for NESs” has been identified as the top priority causal challenge that significantly impacts the effect group challenges. Further, based on the findings of IVFFN-DEMATEL, short-, mid-, and long-term action plans are proposed for real-time decision-makers to improve the existing situations. The output of the study may contribute robustly to the real-time scenarios of NESs by supporting decision-makers, researchers, government authorities, and stakeholders towards developing sustainable CE-based NESs by integrating I4.0 technologies, which will help to achieve a carbon-neutral economy in emerging economies.

An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application

<p>The transformation of traditional hospitals to a smart healthcare system is a patient-centric, reliable, and smarter way to enhance healthcare facilities for individuals. These healthcare devices are based on the Internet of Things (IoT) and conventional network structures such as 3rd generation (3G) or 4th generation (4G). That did not provide the patient health data in the real-time scenario. Therefore, the integrated 5G-IoT system offers distinct benefits such as remote monitoring, surgery, and real-time data analysis of healthcare system that is operated with slice of dedicated network. It generates huge data with the billions of healthcare equipment that can be evaluated for decision making. This enormous data requires low latency, high-security, capacity, and more reliable techniques for evaluation. In this scenario, network slicing is one of the key solutions that can be considered for isolated end-to-end network structures. Network slicing enables logical and independent network virtual networks to be multiplexed for the same physical network. In this article, we propose an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. We examine the performance of the machine learning algorithm by an automatic model, named A Fast Library for automated machine learning (FLAML). In addition, we perform network slicing to obtain resource optimization in 5G-IoT-based healthcare application.<strong></strong></p>

Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping

The advent of temporally dense radar data such as the Sentinel-1 SAR have opened the door for rapid forest disturbance detection in the humid tropics. Tropical dry forest disturbance detection, however, were challenged by seasonality and more open canopy characteristics. In this manuscript, we proposed a Sentinel-1 SAR and deep learning based rapid forest disturbance detection approach for tropical dry forests. We demonstrated a weakly supervised method for reference label harvesting based on medium resolution globally available forest and forest disturbance maps. We trained a deep neural network model to derive forest and forest disturbance probabilities from Sentinel-1 images in the first step. We then implemented a probabilistic disturbance detection and refinement method to map forest disturbances in near real-time in two test regions in Paraguay and Mozambique. We mapped new forest disturbances in an emulated near real-time scenario for 2020 and 2021 and evaluated the spatial accuracy of the disturbance alerts by generating area adjusted precision, recall and F-1 score. We also evaluated the improvement in timeliness of disturbance detection by estimating mean time difference of disturbance events detection with that of Landsat-based GLAD alerts. The generated alerts in Paraguay and Mozambique achieved a precision, recall and F-1 score of 0.99, 0.61, 0.75 and 0.97, 0.51, 0.66, respectively. The proposed method detected disturbances with a mean of 21 days (± 18 days) earlier in Paraguay and 18 days (± 18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.

Workplace spirituality: the game changer of 21st century workplace

To bridge the knowledge gap between the research field and practical organizational setting. WPS is gaining popularity among researchers but lacks real-time practice. This study provides an overview and intellectual structures of WPS on which researchers can further develop modules to practice WPS in real-time scenarios. The authors studied the intellectual structure and emerging themes of workplace spirituality using bibliometric techniques. This is a bibliometric study using scientific data extracted from the Scopus/Web of Science database with the topic "workplace spirituality". Using VOSviewer and RStudio, performance metrics and science mapping have been generated to identify potential concepts that can be incorporated in the workplace. Recent trends, thematic evolution, and thematic maps of WPS have been identified by influential constituents. WPS influential constituents can be useful for future research, as they display all influential matters, such as authors, countries, and affiliations. Science mapping displays visualizations of recent trends and thematic concepts of WPS, which can be useful to identify pathways to inculcate WPS in real-time organizational settings. The literature holds several empirical, review and clinical evidence on workplace spirituality, but this study attempts to perform bibliometric research on the WPS to date as a knowledge update mechanism.

Aural based scene understanding for sonar applications

Acoustic returns scattered from either surfaces or objects are typically interpreted using image processing techniques such as beamforming and computer vision models such as convolutional neural networks. However, image representations can be burdensome to generate, creating delayed responses in real-time scenarios, and difficult to parse from the scene-understanding perspective. This work investigates direct exploitation of the information content from streaming, near-raw sonar time series data for the purpose of scene characterization. Summary statistics drawn from the sonar performance estimation community and communities that study aural-based perception are used to identify scene characteristics such as flat sand, ripples, gravel beds, or sea grass, in side-looking synthetic aperture sonar (SAS) data. An empirical multi-class classification study is presented that explores the performance of seafloor characterization methods on time series versus imagery.

Self-aware face emotion accelerated recognition algorithm: a novel neural network acceleration algorithm of emotion recognition for international students.

With an increasing number of human-computer interaction application scenarios, researchers are looking for computers to recognize human emotions more accurately and efficiently. Such applications are desperately needed at universities, where people want to understand the students' psychology in real time to avoid catastrophes. This research proposed a self-aware face emotion accelerated recognition algorithm (SFEARA) that improves the efficiency of convolutional neural networks (CNNs) in the recognition of facial emotions. SFEARA will recognize that critical and non-critical regions of input data perform high-precision computation and convolutive low-precision computation during the inference process, and finally combine the results, which can help us get the emotional recognition model for international students. Based on a comparison of experimental data, the SFEARA algorithm has 1.3× to 1.6× higher computational efficiency and 30% to 40% lower energy consumption than conventional CNNs in emotion recognition applications, is better suited to the real-time scenario with more background information.

A Virtual Prototyping Platform for Exploration of Vehicular Electronics

A critical requirement for robust, optimized, and secure design of vehicular systems is the ability to do system-level exploration, i.e., comprehend the interactions involved among ECUs, sensors, and communication interfaces in realizing system-level use cases and the impact of various design choices on these interactions. This must be done early in the system design to enable the designer to make optimal design choices without requiring a cost-prohibitive design overhaul. In this paper, we develop a virtual prototyping environment for the modeling and simulation of vehicular systems. Our solution, is modular and configurable, allowing the user to conveniently introduce new system-level use cases. Unlike other related simulation environments, our platform emphasizes coordination and communication among various vehicular components and just the abstraction of the necessary computation of each electronic control unit. We discuss the ability of to explore the interactions between a number of realistic use cases in the automotive domain. We demonstrate the utility of the platform, in particular, to create real-time in-vehicle communication optimizers for various optimization targets. We also show how to use such a prototyping environment to explore vehicular security compromises. Furthermore, we showcase the experimental integration and validation of the platform with a hardware setup in a real-time scenario.

Polyphonic Sound Event Detection Using Mel-Pseudo Constant Q-Transform and Deep Neural Network

The task of identification of sound events in a particular surrounding is known as Sound Event Detection (SED) or Acoustic Event Detection (AED). The occurrence of sound events is unstructured and also displays wide variations in both temporal structure and frequency content. Sound events may be non-overlapped (monophonic) or overlapped (polyphonic) in nature. In real-time scenarios, polyphonic SED is most commonly seen as compared to monophonic SED. In this paper, a Mel-Pseudo Constant Q-Transform (MP-CQT) technique is introduced to perform polyphonic SED to effectively learn both monophonic and polyphonic sound events. A pseudo CQT technique is adapted to extract features from the audio files and their Mel spectrograms. The Mel-scale is believed to broadly simulate human perception system. The classifier used is a Convolutional Recurrent Neural Network (CRNN). Comparison of the performance of the proposed MP-CQT technique along with CRNN is presented and a considerable performance improvement is observed. The proposed method achieved an average error rate of 0.684 and average F1 score of 52.3%. The proposed approach is also analyzed for the robustness by adding an additional noise at different Signal to Noise Ratios (SNRs) to the audio files. The proposed method for SED task has displayed improved performance as compared to state-of-the-art SED systems. The introduction of new feature extraction technique has shown promising improvement in the performance of the polyphonic SED system.

Synthesis of rare-earth metal-organic frameworks to construct high-resolution sensing array for multiplex anions detection, cell imaging and blood phosphorus monitoring

Accurate detection and differentiation of multiple anions is still a difficult problem due to their wide variety, structural similarity, and mutual interference. Hence, four rare-earth metal-organic frameworks (RE-MOFs) including Dy-MOFs, Er-MOFs, Tb-MOFs and Y-MOFs are successfully prepared by using TCPP as the ligand and rare-earth ions as the metal center via coordination chelation. It is found that 7 anions can light up their fluorescence. Thus, a high-resolution sensing array based on RE-MOFs nanoprobes is employed to differentiate these anions from intricate analytes in real-time scenarios. The distinctive host-guest response promotes the RE-MOFs nanoprobes to selectively extract the target anions from the complex samples. By taking advantage of the cross-response between RE-MOFs nanoprobes and anions, it allows to create an array for detecting target analytes using pattern recognition. Additionally, RE-MOFs nanoprobes also facilitate the quantitative analysis of these anions (PO43−, H2PO4−, HPO42−, F−, S2−, CO32– and C2O42−). More importantly, the exceptional effectiveness of this method has been demonstrated through various successful applications, including quality monitoring of 8 toothpaste brands, intracellular phosphate imaging, and blood phosphorus detection in mice with vascular calcification. These findings provide robust evidence for the efficacy and reliability of the RE-MOFs nanoprobes array for anion recognition.

Design and Testbed Experiments of User Authentication and Key Establishment Mechanism for Smart Healthcare Cyber Physical Systems

Smart healthcare technologies transform the traditional healthcare system in all possible ways. Smart healthcare has enormous number of benefits over the traditional system. However, at the same time, it also suffers from some healthcare data security and privacy related issues as the potential Internet attackers may get access to the sensitive healthcare through the deployment of various kinds of attacks. To mitigate these issues, in this paper, a new lightweight remote user authentication and key establishment scheme (in short, UAKM-SH) has been proposed by making the use of lightweight cryptographic operations. The security analysis of UAKM-SH is conducted to prove its robustness against various possible attacks. A detailed comparative study among the proposed UAKM-SH and other existing schemes shows that UAKM-SH performs better in terms of computation cost, communication cost, and the offered security and functionality features. Finally, the testbed implementation of UAKM-SH is given to observe its behavior in the real time scenario.

SCANet: Securing the Weights With Superparamagnetic-MTJ Crossbar Array Networks.

Deep neural networks (DNNs) form a critical infrastructure supporting various systems, spanning from the iPhone neural engine to imaging satellites and drones. The design of these neural cores is often proprietary or a military secret. Nevertheless, they remain vulnerable to model replication attacks that seek to reverse engineer the network's synaptic weights. In this article, we propose SCANet (Superparamagnetic-MTJ Crossbar Array Networks), a novel defense mechanism against such model stealing attacks by utilizing the innate stochasticity in superparamagnets. When used as the synapse in DNNs, superparamagnetic magnetic tunnel junctions (s-MTJs) are shown to be significantly more secure than prior memristor-based solutions. The thermally induced telegraphic switching in the s-MTJs is robust and uncontrollable, thus thwarting the attackers from obtaining sensitive data from the network. Using a mixture of both superparamagnetic and conventional MTJs in the neural network (NN), the designer can optimize the time period between the weight updation and the power consumed by the system. Furthermore, we propose a modified NN architecture that can prevent replication attacks while minimizing power consumption. We investigate the effect of the number of layers in the deep network and the number of neurons in each layer on the sharpness of accuracy degradation when the network is under attack. We also explore the efficacy of SCANet in real-time scenarios, using a case study on object detection.

Reduced Nodal Admittance Matrix Method for Probabilistic GIC Analysis in Power Grids

Efficient probabilistic geomagnetically induced current (GIC) analysis in power grids provides tools for assessing and mitigating small-probability tail risks of geomagnetic disturbances, especially in early warning and real-time scenarios. This letter employs the reduced nodal admittance matrix (RNAM) to speed up GIC calculation based on Kron reduction. Moreover, the proposed RNAM method is used to achieve a more efficient analysis of probabilistic GICs, which considers the uncertainty of the substation grounding resistances. The novel method is compared with the classical algorithms including the nodal admittance matrix method, the Lehtinen-Pirjola method, and the bus admittance matrix method, and its efficiency improvement is illustrated with several power grid test cases.

Shorter latency of real-time epileptic seizure detection via probabilistic prediction

Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly short detection latency in real-time scenarios. In this manuscript, we propose a novel deep learning framework intended for shortening epileptic seizure detection latency via probabilistic prediction. We are the first to convert the seizure detection task from traditional binary classification to probabilistic prediction by introducing a crossing period from seizure-oriented EEG recording and proposing a labeling rule using soft-label for crossing period samples. And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples. Furthermore, we also propose rectified weighting strategy to enhance predictive probabilities, and accumulative decision-making rule to achieve significantly shorter detection latency. We implement the proposed framework on two prevalent datasets — CHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset in patient-specific leave-one-seizure-out cross-validation scheme. Eventually, the proposed algorithm successfully detected 94 out of 99 seizures during crossing period and 100% seizures detected after EEG onset, averaged 14.84% rectified predictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection latency, 0.08/h false detection rate (FDR) on CHB-MIT dataset. Meanwhile, 84 out of 89 detected seizures during crossing period, 100% detected seizures after EEG onset, 16.17% RPIP errors, 4.7 s detection latency, and 0.08/h FDR are achieved on SWEC-ETHZ dataset. The obtained detection latencies are at least 50% shorter than state-of-the-art results reported in previous studies.