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Peer-to-peer (P2P) trading in a local energy market (LEM) offers various participants the opportunity to negotiate and strike energy deals among themselves using a distributed ledger technology called blockchain. In this paper, a new local model is presented using a layer-2 scalability solution for second-generation (Gen2) blockchain technology to enable P2P trading among four types of participants: consumers, prosumers with solar photovoltaic (PV) systems, prosumers with solar PV systems and battery energy storage systems (BESSs), and electric vehicles (EVs). The proposed LEM trading platform involves several critical steps, including the creation of typical forecasting profiles for load consumption, solar generation, and battery state-of-charge (SOC) through a forecasting solution. Next, the LEM participants place their pricing bids using a trading agent service, and the trading engine collects the profiles data and bid prices, which performs matchmaking in a forward-facing market. The output of the trading engine consists of dispatch signals for prices and energy values that are sent to each participant to execute actual trading. Furthermore, the trading engines store the accepted and past bidding data and energy values of P2P trades for each participant in blockchain technology, which can be retrieved and displayed on the LEM user interface screens of participants and administrators using their blockchain addresses at any time during the trading process. This study focuses on simulating proposed LEM models, incorporating functional limitations and market rules. These rules aim to reduce energy costs, enhance margins for utilities and retailers, and mitigate grid congestion through BESSs, resulting in reduced operational and capital expenditure. LEM outcomes are analysed and compared with a Business-as-usual (BAU) model. Participants’ energy trading behaviour, cost-revenue dynamics, grid impact, and blockchain implementation costs are explored. The study highlights LEM benefits in terms of reduced CO2 emissions by 984 kg CO2, increased self-sufficiency by 2.2%, and improved financial benefits of all participants by 21.6%. The use of modern blockchain technology guarantees secure data storage and rapid, cost-effective energy trading, thereby making the proposed LEM platform a viable solution in the distribution market.

This paper proposes the joint use of caching and simultaneous wireless information and power transfer (SWIPT) to enhance the performance of cell-edge users of down-uplink simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-empowered non-orthogonal multiple access (NOMA) systems. The main idea is to leverage the available caching at nearby users to establish cooperative communication while taking into account the problem of energy issues through SWIPT mechanisms, namely time-switching (TS) and power-splitting (PS) strategies. To improve the communication quality of users, STAR-RIS with an energy-splitting (ES) protocol is established and phase-shift design is formulated. Closed-form approximations and asymptotic expressions for users’ outage probability (OP) and effective ergodic capacity (EC) are derived. Based on the attained expressions, useful insights into system design, such as the impact of power allocation, imperfect successive interference cancellation, the number of transmit/reflect components on the system performance, trade-offs between the network requirement and the system performance, and trade-offs between the energy harvesting process and data transmission quality, are deduced. Numerical examples verify our derivations and show that: i) the performance of cell-edge users is improved significantly; ii) exploiting SWIPT-PS shows better OP and EC performance for nearby users compared to SWIPT-TS, while both provide comparable performance for cooperative communication; iii) as the performance of cell-edge users is mostly guaranteed, the communication quality of nearby users in downlink and source node in uplink can be improved by optimizing either the power budget or the energy harvesting factors; iv) adopting STAR-RIS with ES protocol outperforms mode-switching and time-splitting ones in terms of both OP and EC; and v) STAR-RIS-empowered NOMA has better EC performance than orthogonal-multiple access counterparts when SIC operation is properly designed.

Logistics transports, stores, and delivers goods from the producer to the final user. Logistics have become increasingly complex in today’s globalized world, making it imperative to address data integrity, transparency, and secure storage challenges. IoT devices in logistics allow for real-time monitoring of goods, vehicles, and environmental conditions. However, this generates vast amounts of data, necessitating a reliable and secure data storage and management system. These issues above can be addressed by deploying a blockchain-based solution. Blockchain is an innovative technology that operates on a decentralized database system, and it has different applications, which include finance, healthcare, and so on. This research proposed a blockchain- IoT-based Model for enhancing the logistics process. The proposed model utilized the Interplanetary file system to secure and efficiently store logistics data on a distributed and decentralized network and the SHA-256 hashing algorithm to ensure users’ private information anonymity. The model also establishes rules by using smart contracts, which increases efficiency. The performance evaluation of the proposed model was done based on the security transactions, latency, cost, and throughput. The experimental results and performance evaluation show that the proposed model is more efficient and secure than the existing blockchain-based systems. Additionally, the proposed model offers the real-time monitoring of goods while in transit. The proposed model solves the IoT logistics system’s Security, storage, and interoperability challenges. It also provides recommendations for logistics stakeholders to adopt blockchain technology. Despite the implications, the limitation of this study is that it was tested in a controlled environment.

The substantial growth in social media users has led to a significant increase in code-mixed content on social media platforms. Millions of users on these platforms upload pictures and videos and post comments regarding their recent or exciting activities. Responding to this uploaded content, a few users occasionally use offensive language to insult others or specific groups. Social media platforms encounter challenges identifying and removing hate speech and objectionable content in various languages. Hate speech, in its general sense, refers to harmful posts directed at individuals or groups based on factors such as their sexuality, religion, community affiliation, disability, and others. Typically, offensive language is directly or indirectly utilized in hate speech posts to insult someone, causing psychological distress to users. In light of this, we propose developing a system to automatically block, remove, or report posts written in code-mixed Tamil containing hate speech. We have gathered code-mixed Tamil comments from Twitter and the Helo App, categorizing them as hate speech and classifying their intent. We have identified three categories of hate speech intent, namely Targeted Individual (TI), Targeted Group (TG), and Others (O). The Targeted Individual (TI) class encompasses posts aimed at a specific individual target. At the same time, the Targeted Group (TG) category primarily focuses on identifying people based on their religion, community, gender, and other characteristics. The Others (O) category encompasses untargeted offensive posts and other posts containing offensive language. In this context, we propose using a phrase-based, Explainable Hierarchical Attention model for hate speech detection. The results demonstrate that the proposed method is more effective in identifying and explaining hate speech and offensive language in social media posts.

One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot’s behaviour safe but also makes it more energy efficient. In this direction, the variable impedance control (VIC) approach provides a framework for a robot to adapt its compliance during execution by employing an adaptive impedance law. Nevertheless, autonomously adapting the compliance profile as demanded by the task remains a challenging problem to be solved in practice. In this work, we introduce a reinforcement learning (RL)-based approach called DEVILC (Data-Efficient Variable Impedance Learning Controller) to learn the variable impedance controller through real-world interaction of the robot. More concretely, we use a model-based RL approach in which, after every interaction, the robot iteratively learns a probabilistic model of its dynamics using the Gaussian process regression model. The model is then used to optimize a neural-network policy that modulates the robot’s impedance such that the long-term reward for the task is maximized. Thanks to the model-based RL framework, DEVILC allows a robot to learn the VIC policy with only a few interactions, making it practical for real-world applications. In simulations and experiments, we evaluate DEVILC on a Franka Emika Panda robotic manipulator for different manipulation tasks in the Cartesian space. The results show that DEVILC is a promising direction toward autonomously learning compliant manipulation skills directly in the real world through interactions. A video of the experiments is available in the link: https://youtu.be/_uyr0Vye5no.

The coronavirus disease 2019 (COVID-19) epidemic had a momentous influence on the state of universal health and how people live their lives in every nation. The combination of the Internet of Things (IoT) with medical systems is referred to as the “Internet of Medical Things (IoMT),” and it makes it possible for various medical events to take place, including real-time medicine prescriptions, remote patient observing, and real-time diagnosis, amongst other things. However, the security, integrity, and concealment of medical data on the IoMT remain an endless issue, contributing to the difficulties that arise in providing medical services. However, these conventional methods failed to provide robust security against attacks like distributed denial of service (DDoS). As a result, security IoMT-based COVID-19 detection applications are required. Therefore, this article proposes a novel IoMT-based COVID-19 detection and classification network (ICDC-Net) for smart healthcare applications. Initially, Optimal Feistel Block Cipher (OFBC) encryption was performed on COVID-19-based medical data (chest x-ray images), which provides the highest security to the patient’s data. Here, OFBC loss is optimized by the Hybrid Grey-Wolf Optimizer with Particle Swarm Optimization (HGWO-PSO), which performs DDoS attack detection and prevention. Further, the HGWO-PSO is also used to extract disease-specific features from the COVID-19 chest X-ray (CXR) images. Finally, the Residual Network50 (ResNet50) deep learning model is used to classify multiple diseases from CXR images, including normal, COVID-19, and viral pneumonia. The simulations disclosed that the proposed ICDC-Net resulted in improved attack detection accuracy (ADA), attack detection time (ADT), and reduced attack detection error rates (ADER) as compared to other security standards. Further, the simulations also disclosed that the proposed ICDC-Net developed superior COVID-19 categorization performing as equated to existing classification prototypes.

This study addresses the loss minimization and voltage stability analysis for low-frequency transmission lines connected to wind farms, pumped storage Hydropower (PSH) and Gas Turbine plant. The losses and the voltage stability index connected with renewable energy systems are heavily dependent on the operating frequency and create uncertainties in terms of voltage and frequency. Therefore, optimal frequency selection for LF-HVac lines also become important for the system. The voltage stability has been improved by STATCOM (Static Synchronous Compensator) for given system placed at different buses to maintain stability of system. A simplified two-port equivalent model for transmission lines based on solutions to Telegrapher’ method has been proposed for reducing system complexity and losses. The proposed method is tested with different existing algorithms and implemented in IEEE 39-Bus system and for scalability compared with IEEE 57-Bus system. The results obtained in MATLAB environment in Simulation lab which indicate that the proposed LF estimation method with the modified metaheuristic algorithm gives optimal frequency for low-frequency HVac systems.

The digital era has brought significant advancements in technology to such an extent that it has transformed people’s life. Assistive technology makes it possible to undergo tasks that are otherwise difficult for people who are differently-abled. For instance, voice-over capabilities and screen readers make it possible for the visually disabled to use various smartphone applications. Digital payment applications are one such application that significantly eases their daily financial transactions needs. There may, however, be several design issues while using such applications. The mobile applications designed should be designed equitably, keeping in mind the needs of people with disability. A usability study is conducted to assess a product by its actual testing done by a diverse audience. This study aims to evaluate Unified Payments Interface (UPI)-based digital payment solutions for the visually impaired. To identify the design considerations needed while designing digital payment applications, three UPI mobile applications (namely Google Pay, PhonePe, and Paytm) most used in India were selected and evaluated with fifteen visually impaired participants using standard accessibility guidelines. A broad objective of this study is to bring out the issues in accessibility features and suggest insights for re-designing these features to make mobile applications more accessible for visually impaired users. Usability tests were conducted on our volunteers to gather relevant data and a thorough analysis was thereby done. Our analysis reveals that while digital payment mobile applications provide accessibility features for visually impaired users, there may be significant design issues in areas such as screen reader compatibility, keyboard navigation, and clear labelling of buttons and controls. Furthermore, suggestions for improving the accessibility of digital payment mobile applications to serve the visually impaired community can be found based on the findings of the present research study. These suggestions can be useful while designing or updating digital payment mobile applications considering the needs of visually impaired people.

Objective: This study addressed the problem of objectively detecting leaks in P2 respirators at point of use, an essential component for healthcare workers' protection. To achieve this, we explored the use of infra-red (IR) imaging combined with machine learning algorithms on the thermal gradient across the respirator during inhalation. Results: The study achieved high accuracy in predicting pass or fail outcomes of quantitative fit tests for flat-fold P2 FFRs. The IR imaging methods surpassed the limitations of self fit-checking. Conclusions: The integration of machine learning and IR imaging on the respirator itself demonstrates promise as a more reliable alternative for ensuring the proper fit of P2 respirators. This innovative approach opens new avenues for technology application in occupational hygiene and emphasizes the need for further validation across diverse respirator styles. Significance Statement: Our novel approach leveraging infra-red imaging and machine learning to detect P2 respirator leaks represents a critical advancement in occupational safety and healthcare workers' protection.

As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the load balancing and energy-efficient migration model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a deep reinforcement learning-based iterative-learning contextual side chaining model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means clustering and fuzzy logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ bidirectional long short-term memory networks with recurrent graph neural network, for accurate workload prediction and informed migration decision making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This load balancing and energy-efficient migration model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

The success rate of a neural network (NN) classifier (rectified linear unit, 10 layers, softmax output layer activation)-based demodulator was proposed and evaluated for phase-shift keying (PSK) and quadrature amplitude modulation (QAM) modulated signals corrupted by additive white Gaussian, chisquared, uniform, and Rayleigh noise channels with signal-to-noise ratios (SNR) ranging from −20 dB to +20 dB. Low SNR are common in spectrum sensing, cognitive radio networks, underwater acoustics, target detection, remote sensing, seismic monitoring, and helicopter blade detection. This classifier-demodulator performance was compared with that of the matched filter detector (MFD) for varying channel noise, constellation type (PSK or QAM), constellation size (M=2, 4, 8, 16), sample size (N), and training to test the data ratio. The classifier demodulator had a performance equal to or better than MFD in 98% of the scenarios. A training-to-test ratio of 70:30 or 80:20 is appropriate. The classifier performances of M-PSK and M-QAM are comparable. The superior performance of the NN classifier is more pronounced for M values greater than 2. N=5000 or higher is sufficient for most scenarios, and N=20000 is necessary for M=16. A higher success rate was obtained for additive chisquare and Rayleigh noise channels. The proposed demodulator performed significantly better than the matched filter for SNR values $\le0$ dB. 16-QAM over an additive uniform noise channel has a better success rate for an SNR of 0 dB or less, whereas 16-QAM over an additive Rayleigh noise channel has a better success rate for an SNR of 5 dB or higher.

This article presents development of a UAV based frequency modulated continuous wave (FMCW) radar system for remotely sensing the water contained within snowpacks. To make the radar system compatible with the payload requirements of small UAV platforms, the radar electronics are implemented with CMOS technology, and the antenna is implemented as an extremely compact and lightweight metasurface (MTS) antenna. This article will discuss how the high absorption losses of snowpacks lead to dynamic range requirements much stricter than FMCW radars used for automotive and other sensing applications, and how these requirements are met through antenna isolation, leakage calibration and exploitation of the range correlation effect. The article discusses in detail the implementation of the radar system, the CMOS microwave and digital circuitry, and the MTS antenna. The developed radar was mounted on a drone and conducted surveys in both Idaho and Alaska during the 2022-2023 winter season. We present several of those field results.

We investigate a hybrid scheme that is a combination of orthogonal and non-orthogonal multiple access (NOMA) techniques. Specifically, we use Alamouti code for transmission of the symbols; each symbol contains the information of two or more users by utilizing NOMA-based technique. It is shown in the paper that the code rate of the proposed scheme is more than one, and it performs better than the conventional NOMA-based communication system. The closed-form expression of symbol error rate and diversity order is derived by using moment generating function-based approach. It is shown that the power allocation coefficient does not affect the diversity order.

This paper introduces an innovative approach to underwater exploration by integrating Artificial Intelligence (AI) into Autonomous Underwater Vehicles (AUVs). This collaboration between AI and biomimicry marks a new era for AUVs, enabling them to emulate marine creatures’ graceful and efficient movements. By infusing AI capabilities into AUVs, AUVs are empowered to learn and adapt, making autonomous real-time decisions without human intervention. This dynamic integration equips AUVs to effectively navigate complex underwater terrains, evade obstacles, and seamlessly interact with marine life. Inspired by the remarkable propulsion mechanisms found in marine organisms, this work proposes a pioneering propulsion system tailored for AUVs. Taking cues from the locomotion of creatures like cuttlefish, the biomechanics is translated into a robotic propulsion system. The result is a fluid and energy-efficient propulsion method that mitigates the harmful effects of cavitation, thereby reducing noise pollution and minimizing disruption to marine ecosystems. This research evaluates the performance of on-device AI models for analyzing the sensing environment around the AUV and taking real-time images. This automated sensing and navigation method can help the AUVs independently navigate to the desired location along the water table. The propulsion is achieved by building a crankshaft mechanism and a unified mechanical design to convert rotational motion from a motor into a sinusoidal wave motion to replicate the cuttlefish locomotion pattern. The proposed underwater vehicle, Aquabot, is designed using Fusion 360 simulation and ANSYS software. The results demonstrate the accuracy and efficiency of the autonomous underwater vehicle based on the environmental conditions, thus reducing energy consumption and enhancing aquatic vehicle efficiency.

Optical endoscopy is a widely used minimally invasive diagnostic tool for imaging internal organs. The imaging resolution is defined by the numerical aperture of the objective lens. In this study, we proposed and demonstrated a Super-resolution Correlating OPtical Endoscopy (SCOPE) system. In SCOPE, modified recording and reconstruction methods are introduced with the existing built-in endoscopy lens. Instead of recording a single image, multiple images of the object are recorded by scanning the tip of the endoscope around the object. The recorded low-resolution images of the object are arranged as sub-matrices in a 2D matrix. Another similar 2D matrix with either recorded or synthesized point spread functions (PSFs) is created. The 2D matrices of the object and the PSF were processed using a deconvolution algorithm to reconstruct a super-resolution image of the object. Both simulation and proof-of-concept experimental studies have been presented. SCOPE neither requires any additional optical element nor any changes in the endoscopy system itself; therefore, it can be easily implemented in commercial endoscopy systems.

Segmentation of lesions is crucial for the detection and treatment of ischemic stroke. The aim of this work is to develop a robust and highly accurate framework for lesion stroke detection in brain Magnetic Resonance Imaging (MRI). The paper propose a novel deep learning model, named EnigmaNet, for the segmentation of ischemic stroke lesions in Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion Weighted Imaging (DWI) images. EnigmaNet use novel Genesis-k blocks and dual attention mechanism in the encoder and decoder blocks of the architecture. A modified Weighted Focal-Tversky-Dice (wFTD) Loss is used for improved performance. The model is validated on the ISLES-2015 public dataset. EnigmaNet showed a Dice score of 0.8965, sensitivity of 0.8776 and specificity of 0.9866 for the FLAIR test images. Dice score of 0.8423, sensitivity of 0.8452 and specificity of 0.9754 were obtained for DWI images. The segmentation results of EnigmaNet shows an improvement in Dice score of about 32% over U-Net-sharp, 41% over FCN-8 and 10% over Attention U-Net. A region-based comparison of segmentation results of EnigmaNet highlighted its ability to detect fine lesions accurately in multiple vascular territories in brain, thereby signifying its robustness to segment lesions of diverse size, shape and location. The proposed model showed improved results as compared to the state-of-the-art techniques. EnigmaNet model is thus a promising approach for accurate and robust segmentation of ischemic stroke lesions.

In recent times, the proliferation of Internet of Things (IoT) technology has brought a significant shift in the digital transformation of various industries. The enabling technologies have accelerated this adoption. The possibilities unlocked by IoT have been unprecedented, leading to the emergence of smart applications that have been integrated into national infrastructure. However, the popularity of IoT technology has also attracted the attention of adversaries, who have leveraged the inherent limitations of IoT devices to launch sophisticated attacks, including Multi-Stage attacks (MSAs) such as IoT botnet attacks. These attacks have caused significant losses in revenue across industries, amounting to billions of dollars. To address this challenge, this paper proposes a system for IoT botnet detection that comprises two phases. The first phase aims to identify IoT botnet traffic, the input to this phase is the IoT traffic, which is subjected to feature selection and classification model training to distinguish malicious traffic from normal traffic. The second phase analyses the malicious traffic from stage one to identify different botnet attack campaigns. The second stage employs an alert correlation approach that combines the Latent Semantic Analysis (LSA) unsupervised learning and graph theory based techniques. The proposed system was evaluated using a publicly available real IoT traffic dataset and yielded promising results, with a True Positive Rate (TPR) of over 99% and a False Positive Rate (FPR) of 0%.

We propose a Transfer learning-enabled electroencephalography-based intuitive brain-computer interface system by utilizing phase-based brain functional connectivity methods such as phase lag index (PLI) and Intersite phase clustering (ISPC) along with power features to explore both phase and power-based information from electroencephalography (EEG) signals. Time-frequency decomposition using a complex morlet wavelet is applied to analyze the signal components in both the time and frequency domains and extract phase connectivity and power features. Functional connectivity methods aim to recognize functional interactions and statistical mutuality among signals acquired across various brain areas. The phase-based connectivity features are extracted simultaneously for multiple channels to investigate the phase synchronization among EEG signals across the entire brain. Next, Graph theory is adopted to trace connectivity between brain regions by calculating the connectivity degree of extracted PLI and ISPC features with other electrodes. In Parallel, Discrete wavelet convolution is performed to calculate the time variable frequency band’s specific power from the imagined speech EEG data. Finally, Time-frequency images of the above-mentioned PLI, ISPC, and EEG power features are fed as input to DenseNet-121 architecture for classification. Dense Net architecture overcomes the problem of ‘vanishing gradient’ by connecting each layer directly with other layers, making the network densely connected. The maximum classification accuracy achieved is 100%, 99.14%, and 98.72% for binary, three-class, and four-class classifications, respectively. The experimental results indicate that the proposed phase-based connectivity features, EEG power, and the DenseNet-121 model have achieved excellent accuracy for two public datasets, outperforming the state-of-the-art methods. The outstanding results strengthen the possibility of real-time EEG-based intuitive brain-computer interface communication.

Robust Intrusion Detection Systems (IDS) are increasingly necessary in the age of big data due to the growing volume, velocity, and variety of data generated by modern networks. Metaheuristic algorithms offer a promising approach to enhance IDS performance in terms of optimal feature selection. Combining these algorithms along with Machine learning (ML) for the creation of an IDS makes it possible to improve detection accuracy, reduce false positives and negatives, and enhance the efficiency of network monitoring. Our study proposes using metaheuristic algorithms along with machine learning classifiers for feature selection to optimize the number of features from the data set of computer network traffic. We have tested several combinations of algorithms viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) along with ML algorithms viz., Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) and Logistic Regression (LR). The combinations of algorithms have been tested over the NSS-KDD and kddcupdata_10% data sets. We have drawn several insights on feature selection scores with respect to test scores, FI scores, recall and precision for various algorithm combinations. The feature selection time has also been highlighted to showcase the fastest-performing algorithm combinations. Ultimately, we have presented three combinations of algorithms depending on organizational IDS requirements and provided separate solutions for each.

The rapid advancements in electric vehicle technology have elevated the lithium-ion battery to the forefront as the paramount energy storage solution. The battery’s health tends to deteriorate gradually as it ages. Due to the inevitable physiochemical reactions that take place inside the battery, it undergoes degradation and at a certain point, it becomes unserviceable. The battery degradation can be estimated using state of health (SOH). This paper employs data-driven techniques to estimate the state of health (SOH) of a battery. To estimate health parameters, a vast quantity of data, such as voltage, current, and temperature, is gathered from the NASA Prognostics Center of Excellence. The data is resampled using the superior Fourier Resampling method and then fed to a machine-learning algorithm. In this study, SOH estimation is carried out using three different machine-learning techniques i.e. Long Short Term Memory (LSTM), Deep Neural Networks (DNN), and Gated Recurrent Unit (GRU). However, the performance and accuracy of SOH estimation using these algorithms are highly dependent on hyperparameter tuning. Therefore, the optimal hyperparameter tuning has been adopted in the present work to reduce the time and complexity of the estimation. Further, the performance of various proposed techniques has been compared against each other using different performance indices such as root mean square error (RMSE), mean absolute error (MAE), and R-square error. GRU technique proved to be excelling with RMSE of 0.003, MAE of 0.003, and R-square error of 0.004 while estimating the SOH of various samples of batteries. This detailed analysis will be helpful for users to evaluate the performance of a battery and plan for maintenance accurately and effectively with minimum downtime.

The burgeoning field of neurodegenerative disease detection and management necessitates the development of robust and comprehensive diagnostic approaches. Existing methodologies often fall short in effectively capturing the complex interplay of brain signals and genetic markers, which are crucial in the early detection and progression tracking of such diseases. This paper introduces a novel multimodal framework that leverages advanced signal processing and machine learning techniques to address these limitations, providing a more accurate and holistic understanding of neurodegenerative diseases. Our proposed model integrates multiple modalities: EEG signal analysis using Time-Frequency Analysis and Wavelet Transform, functional Magnetic Resonance Imaging (fMRI) analyzed through Independent Component Analysis (ICA) and Correlation Analysis, Magnetoencephalography (MEG) employing Beamforming and Source Localization Techniques, and Genomic Data analysis using Graph Neural Network for Genetic Pattern Recognition process. This integration is realized through the fusion of modalities using Gated Recurrent Units (GRU) and the classification into disease classes via an efficient 1D Convolutional Neural Network (CNN). The reasons for selecting these methods are twofold: they address the non-stationary characteristics of EEG signals and exploit spatial information of brain activity, while also identifying functional networks and genetic patterns associated with neurodegeneration conditions. The clinical impact of this work is profound. Tested on the BioGPS and BrainLat datasets, our framework demonstrated a 10.4% increase in precision, 8.5% increase in accuracy, 8.3% increase in recall, 9.4% increase in the Area Under the Curve (AUC), 7.5% increase in specificity, and a 2.9% reduction in delay compared to existing methods.

There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (GAs) proved to be quite helpful in general but proved to be poor when it comes to fast convergence and adaptability to the changing communication environment. Moreover, fault tolerance and resource efficiency in these communication channels are poorly addressed, giving rise to a huge roadblock to practical implementations in a number of different use cases. This research presents a set of novel hybrid optimization methods aimed at addressing the issues previously mentioned. In the first place, a Quantum-inspired Genetic Algorithm (QIGA) with Simulated Annealing (SA) Initialization is proposed through augmenting traditional genetic operations with the quantum computing paradigm. In the first instance, the hybrid approach works by utilizing the parameters derived from SA while initializing the GA population, hence effective exploration of solution space. Additionally, the Adaptive Genetic Algorithm with Reinforcement Learning (AGA-RL) is presented, which uses real-time feedback from the current environment to modify the GA parameters. This way, the system will be much more adaptive and hence be able to adjust the GA so that it can match the rapidly changing channel conditions. Simultaneously, the Fault-Tolerant Genetic Algorithm with Error-Correcting Codes (FTGA-ECC) is developed to ensure reliable data transmission, especially in an error-prone environment. To make up for resource constraints, the Energy-Aware Genetic Algorithm with Dynamic Resource Allocation (EAGA-DRA) strategy ensures that resources are well-managed by dynamically changing allocation based on the optimization process and the particular needs of the channel, hence reducing energy overhead and spatial overhead without compromising channel quality. A great leap is in the integration of Machine Learning through the Deep Reinforcement Learning-guided Genetic Algorithm (DRL-GA). The above approach exploits deep reinforcement learning to fine-tune the exploration and exploitation strategies of the GA, hence providing faster convergence and improved solution quality. All of these methods are validated by the Quantum-inspired Benchmarking Framework (QIBF), which forms a systematized comparative platform to evaluate different optimization techniques from the perspective of QCA-based communication systems. The above approaches not only provide the improvement in efficiency and reliability but also contribute to enhancing the adaptability of communication systems and provide a very good base for future research and development in the field of quantum communications and beyond, with potential applications extending to secure communication, quantum computing, and nanotechnology-based devices & scenarios. This work is multifold, promising significant advancements in the field of quantum communication and beyond. The empirical findings showed that the suggested algorithms perform significantly better than traditional approaches in several important performance metrics, such as robustness to channel errors, adaptability to changing signal-to-noise ratios, convergence speed under dynamic conditions, and solution quality in highly variable environments.INDEX TERMS Quantum-inspired genetic algorithm, adaptive genetic algorithm, fault-tolerance, energy-efficient optimization, deep reinforcement learning.

The ongoing changes in modern power systems towards increasingly decentralized systems render the coordination of generation assets and the corresponding dependency on Information and Communication Technology highly relevant. This work demonstrates the impact of three types of ICT errors, namely delayed data, data loss and data corruption, on the control of distributed energy resources in an active distribution network. The settling time of the active power response at the interconnection point between the distribution and transmission system is investigated in the simulations. Additionally, two fallback strategies to mitigate the impact of data loss are proposed and evaluated with regard to their impact on the controller’s response. Finally, a generalized, aggregated service state description is proposed in order to capture the performance of the active distribution network service. It is meant to improve the interpretability of the results, which can be used to compare service designs and setups.

Vehicular ad hoc networks (VANETs) are self-organizing, open-structure inter-vehicle communication networks, along with wireless communication technology and transportation. Vehicle communication aims to improve the driver’s response-ability and ensure traffic safety when encountering traffic accidents. The 5G NR on V2V communication, compared to fourth-generation (4G) long-term evolution (LTE), is more sophisticated and has ultra-high reliability. This research proposes intelligent driver model-lane changes (IDM-LC) and intelligent driver model-avoidance (IDM-A) models, which enhance the performance accuracy of the V2V communication through the 5G NR system. In this case, the authenticity of the vehicle movement models is tested on different road scenarios, i.e., square, hexagon, heptagon and triangle. In these scenarios, the vehicles’ movements over the 5G networks receive widespread coverage whenever the vehicles receive signals from the roadside unit (RSU). Hence, the flexibility of VANET is used to improve the device-to-device (D2D) or vehicle-to-vehicle (V2V) communication efficiency in the fifth-generation (5G) new radio (NR) system. In addition, the VANET simulation platform, i.e., simulation of urban mobility (SUMO) and network simulator-3 (NS-3), simulate and evaluate the comparison of V2V through the 5G networks, which receives widespread coverage in 15 to 20 m/s. The simulation results and analysis show that the V2V communication through the 5G system performs better on the IDM models.

Internet of Things (IoT) has proved its applicability in numerous domains, such as healthcare, agriculture, automobile, industrial production, logistics, and supply chain management. Looking at the current trend, we expect a massive proliferation of such IoT devices all around us. However, one of the issues with the widespread use of IoT is the increasing complexity of the underlying architecture, which leads to difficulty in ensuring security compliance. Key refreshment, one of the critical aspects of key management, requires a regular update of the key used to provide strong security. However, in most cases, keys are not updated. If they are updated, the update logs (e.g., time of last key update) are not available for all entities to verify and establish trust in the system. Moreover, the rules for key refreshment are also not defined transparently. In this direction, the present work proposes a blockchain-regulated, secure, verifiable, and automatic key refreshment mechanism for IoT systems. The proposed mechanism enables users to verify the freshness of the security keys (being used), thereby relying on the data from IoT devices and establishing trust in an IoT system. The proposed mechanism is driven by blockchain technology and smart contract. As proof of concept, we have implemented the proposed solution using Ethereum and Hyperledger Fabric blockchains. Cost, scalability, and security (formal and informal) analyses have been carried out for performance analysis. The results show the economic viability and strong security of the proposed mechanism.

Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoT-based HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes’ energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme- An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters’ formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network. EEHCT establishes its supremacy over state-of-the-art schemes via an extensive set of simulations and experimentation in terms of multiple network performance metrics like stability, throughput, and network lifetime. Like, it achieves a gain up to 90.27% with respect to network lifetime over its peers in the standard operating conditions and under varying network configurations. In addition to quantitative analysis, a statistical analysis has also been provided to demonstrate the formation of energy-balanced clusters through the proposed scheme.

This proposed work describes a miniaturized and simple power divider that uses coupled lines for a triple-band filtering response. The intended setup includes an isolation resistor connecting the output feed lines through Structure-3 and coupled line-based microstrip resonators connected to the input feed line. The presented power divider achieves three passbands with a filtering response. The first and second passbands are achieved by the coupled line structure that includes Resonator-1 and Resonator-2. The third passband is realized due to Resonator-3. Furthermore, by varying the structural parameter of the designed triple-band filtering power divider, the resonance frequencies of all three passbands can be controlled because of changes in their equivalent lumped parameters. The resonance frequency mainly depends on its equivalent inductance and capacitance value. For validation of the simulation result, the filtering power divider structure has been fabricated and tested. The experimental results denote that the prototype has a fractional bandwidth ( $\vert \text{S}21\vert < $ -3 dB) of about 20.0 % (1.1 GHz to 1.35 GHz), 37.0 % (2.0 GHz to 2.9 GHz), and 15.0 % (3.25 GHz to 3.8 GHz) respectively for first, second and third pass-bands respectively. This arrangement shows isolation above 18 dB for all three bands with a downsized of $0.10\lambda _{g}\times 0.12\lambda _{g}$ (25 mm $\times 30$ mm) according to the operating frequency of the first frequency passband. The triple band performance of the developed component shows a good input reflection coefficient. The operating frequencies, which are suitable for the application of the $L$ -band, WLAN, sub-6 GHz band of 5G, include 1.23 GHz, 2.41 GHz, and 3.55 GHz.

In the next generation, emerging network technologies like Software Defined Networking (SDN) and Wireless Sensor Networks (WSNs) will be developed and deployed to improve computing facilities. Micro-electromechanical system (MEMS) technologies advance, and WSNs gain in popularity because they provide real-time monitoring solutions that are both economically and practically viable. The SDN paradigm is consequently being incorporated into WSN to prevent a performance bottleneck with traditional network architecture as network traffic and diverse sensor nodes increase. Because they interact through a public channel and the sensor nodes are spread throughout a hostile environment, the information transmitted among entities is subject to assault. The proposed protocol showed how centralized SDN controller nodes logically assumed the role of network management and control. Therefore, we present a Lightweight Authentication and Key Agreement Protocol (LAKP) for SDN-enabled WSNs to protect entity communication. Additionally, we demonstrate that the suggested system prevents known security flaws by conducting both informal and formal security studies using the Scyther tool and Burrows-Abadi-Needham (BAN) logic. Further, the performance study demonstrates that the suggested scheme performs better in computing and communication burdens than related protocols with 1.6% to 5.4% and 1.3% to 5.8%, respectively.

The genes data produced by microarray experiments is complex in terms of dimensions and samples. It consumes a lot of computation power and time when it is processed for a disease analysis while working with an expert system. At the same time, data can help doctors identify a patient’s health condition if it is presented in a meaningful way and processed on time. Several methods have been proposed to reduce the dimensions of medical microarray data and optimize its search space with minimal accuracy loss. However, the discretization of continuous gene-values in the process of dimension reduction is failed to preserve the inherent meaning of genes. Also, ensuring high accuracy and interpretability in the reduction process may result in extra processing time, which is unfavorable for time-critical applications. To overcome these issues, in this paper, we propose a dimension reduction method in conjunction with a fuzzy expert system (FES) optimization approach, while keeping an accuracy-interpretability-speedy tradeoff in mind. To accomplish this, we use a fuzzy rough set on ${f}$ -information to identify meaningful genes without changing their original values. We propose a conditionally guided particle swarm optimization for faster knowledge acquisition, where the velocity is adjusted based on a predefined update probability, resulting in a faster search. A big data processing architecture is designed using the Hadoop ecosystem along with a $MapReduce$ -equivalent algorithm of the proposed method for speedy processing, enabling parallel processing on microarray data to reduce dimensions and perform classification through knowledge extraction. The proposed method is thoroughly tested on eleven microarray datasets by considering accuracy-interpretability-speed tradeoff. The results show that the proposed method is effective in identifying disease-causing genes while also understanding the patient’s genetic profile with only a few operations and a small amount of CPU time. Statistical tests are also run to validate the proposed method’s efficacy in comparison to other methods.

Machines are continually being channelized in the current era of automation to deliver accurate interpretations of what people communicate on social media. The human species is today engulfed in the concept of what and how people believe, and the decisions made as a result are mostly dependent on the sway of the masses on social media platforms. The usage of internet as well as social media is booming day by day. Today, this ocean of data can be used for the fruitful purposes. Analysis of social media sentiment textual posts can supply knowledge and information that can be used in citizen opinion polling, business intelligence, social contexts, and Internet of Things (IOT)-mood triggered devices. In this manuscript, the main focus is the sentiment analysis based on Emotional Recognition (ER). The proposed system highlights the process of gaining actual sentiment or mood of a person. The key idea to this system is posed by the fact that if smile and laughter can be two different categories of being happy, then why not happpyyyyyy and happy. A novel lexicon based system is proposed that considers the lengthened word as it is, instead of being omitted or normalized. The aggregated intensified senti-scores of lengthened words are calculated using framed lexicon rules. After that, these senti-scores of lengthened words are used to calculate the level of sentiment of the person. The dataset used in this paper is the informal chats happened among different friend groups over Facebook, Tweets and personal chat. The performance of proposed system is compared with traditional systems that ignore lengthened words and proposed system outperform tradition systems by achieving 81% to 96% F-measure rate for all datasets.

Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, $\pi $ /4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.

Indoor Localization is gaining increased importance due to numerous location-based services in healthcare, logistics, and security, to name few, that are expected to be provided by next-generation wireless networks. Such services are characterized by stringent accuracy requirements, short response time, and lower cost which makes the localization problem more challenging and deserving of attention. A key element of the localization process is distance estimation (also known as ranging). In this paper, we design and analyze an efficient decimeter-level two-way ranging scheme for ubiquitous WiFi networks in the 5 GHz frequency band whose accuracy approaches ideal one-way ranging with no phase mismatches. We investigate the idea of channel frequency response (CFR) stitching across non-contiguous WiFi channels and how two-way CFR measurements help in achieving the CFR coherency necessary for accurate ranging. In addition, we quantify the decrease in ranging accuracy of two-way compared to one-way ranging due to SNR degradation, Line-of-Sight (LoS) component shrinkage, and doubling the multipath delay spread. Furthermore, We design a novel scheme to bridge the performance gap between two-way ranging and ideal one-way ranging which operates in three main steps: square-root of the two-way CFR, followed by phase unwrapping, and finally deep fade detection and phase errors correction. Our proposed scheme achieves significant performance gains over two-way ranging with only a slight performance gap from ideal one-way ranging. Moreover, our proposed scheme enjoys robustness as it preserves the ranging accuracy gains in various WiFi communication scenarios when operating at different SNR levels, different multipath channel models, and different CFR bandwidths, as well as operating under system impairments such as Sample Timing Offset (STO). The accuracy gains achieved by the proposed schemes are demonstrated using both simulations and an in-house WiFi testbed. Finally, we quantify the added complexity of our proposed scheme and show it to be insignificant compared to that of the MUSIC super-resolution ranging steps which confirms the practical viability of our proposed scheme.

The potential of digital twin technology to generate virtual models and simulations of real assets and processes has contributed to the widespread adoption of this technology across a variety of business sectors. As a result, performance, cost savings, and overall efficiency have all seen significant improvements. By conducting a study of the academic literature and previous research, this article offers readers an introduction to the origins, evolution, and applications of digital twin technology. This paper looks at the different kinds of digital twins, the advantages, and disadvantages of using them, and the causes that have contributed to their rising popularity. Case examples illustrating effective implementations of digital twin technology in industry and healthcare are included in the article. An overview of digital twin technology and its prospective applications in several industries is provided in this paper. This paper intends to contribute to the current understanding of digital twin technology and its potential impact by discussing the advantages, limitations, and prospects for further study and development.

Optical intelligent reflecting surfaces (OIRS) have the potential to enhance the capacity of the next generation optical wireless communication systems by re-configuring the propagation environment of optical waves. In this work, a submerged OIRS and a planar mirror surface (PMS) are used to build up a non-line of sight (NLOS) underwater wireless optical communication (UWOC) system. An NLOS UWOC setup, which makes use of the total internal reflection (TIR) phenomenon, serves as a benchmark against which the OIRS/PMS-assisted UWOC systems are compared. With the help of the Mellin inverse transform, Meijer’s G function, and Fox’s H-function, the closed-form expressions for the probability density function (PDF), cumulative distribution function (CDF), average spectral efficiency (SE), average energy efficiency (EE), outage probability, and average bit error rate (BER) of the proposed systems are derived. Further, an asymptotic analysis of average SE is performed to illustrate a better understanding of these proposed system’s performance at high SNR. The simulation results indicate that the OIRS-assisted UWOC system shows improved performance in comparison to benchmark scenario. Additionally, the efficient zones of OIRS deployment are analyzed.

Toward the next-generation ultra-long-haul optical network, an extremely gradient boosting (XGBoost)-aided machine learning (ML) model is proposed to maximize the flexibility and uniformity in the performance of distributed Raman amplifier (DRA). In order to achieve an accurate prediction of desired signal gain spectrum and bit error rate (BER), a novel decision-tree based system is employed against inconsistent dimensionality between pump frequency and power. The impact of various model evaluation techniques: mean squared error (MSE), coefficient of determination (R2), root mean square measured data ratio (RSR) and the Nash-Sutcliffe coefficient (NSE) are discussed in detail. It is shown that the proposed method can diagnose the fault within 2.3 ms with accuracy of 99.6% and has also the highest estimation and efficacy in comparison with other ML based tree models. The reported work transforms the successful implementation of XGBoost model to estimate the desired gain profile and BER of DRA in low-loss optical wavelength region (1260–1650 nm).

In this paper, we have developed a series of wave hybrid models for significant wave height prediction. Our developed hybrid models uses a triplet of signal decomposition method, regression model and meta-heuristic algorithm. We have used the $\epsilon $ -Support Vector Regression ( $\epsilon $ -SVR), Least Squares Support Vector Regression (LS-SVR), Long Short-Term Memory (LSTM) and Large-margin Distribution Machine based Regression (LDMR) model for the regression task. For signal decomposition methods, we have considered the Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) method. Apart from this, we have also used the Particle Swarm Optimization (PSO) method to tune the parameters of the used regression model in our wave hybrid models. Till now, the VMD method and LDMR model have not been used in any wave hybrid model. We have evaluated the performance of our developed wave hybrid models on time-series significant wave heights, collected from four different buoys using the different evaluation criteria. After the detailed statistical analysis of the obtained numerical results, we conclude that the VMD-PSO-LDMR based wave hybrid model obtain best performance on six datasets out of seven considered datasets. Also, the VMD based wave hybrid models can obtain better performance than other decomposition based hybrid models. Further, we also conclude from our numerical results that the LSTM model outperforms the SVR, LS-SVR and LDMR based hybrid models if we do not decompose the significant wave height signals apriori. But, when we decompose the SWH time-series signals using a particular decomposition method, then SVR, LS-SVR and LDMR based hybrid models tend to improve their prediction ability significantly.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to have a significant impact on the global population. To effectively triage patients and understand the progression of the disease, a metric-based analysis of diagnostic techniques is necessary. The objective of the present study is to identify COVID-19 from chest CT scans and determine the extent of severity, defined by a severity score that indicates the volume of infection. An unsupervised preprocessing pipeline is proposed to extract relevant clinical features and utilize this information to employ a pretrained ImageNet EfficientNetB5 model to extract discriminative features. Subsequently, a shallow feed-forward neural network is trained to classify the CT scans into three classes, namely COVID-19, Community-Acquired Pneumonia, and Normal. Through various ablation studies, we find that a domain-specific preprocessing pipeline has a significant positive impact on classification accuracy. The infection segmentation mask generated from the preprocessed pipeline performs better than state-of-the-art supervised semantic segmentation models. Further, the estimated infection severity score is observed to be well correlated with radiologists’ assessments. The results confirm the importance of domain-specific preprocessing for training machine learning algorithms.

Advancements in networking technologies have enabled doctors to remotely diagnose and monitor patients using the Internet of Medical Things (IoMT), telemedicine, and edge-enabled healthcare. In e-healthcare, medical reports and patient records are typically outsourced to a server, which can make them vulnerable to unauthorized access and tampering. Therefore, it is crucial to ensure the authorization, security, confidentiality, and integrity of medical data. To address these challenges, this paper proposes a novel reversible watermarking approach with a high payload and low computational cost. First, the input medical image is divided into a Border region (BR) and a Non-Border region (NBR). The NBR region is upscaled using Neighbour Mean Interpolation (NMI) to ensure reversibility. The Electronic Patient Record (EPR) is encrypted using a pseudorandom key, which is generated adaptively from the host medical image and the Enigma machine. The encrypted EPR is then embedded in the medical image using NMI. Two levels of tamper detection (global and local) are performed at the receiver’s end for higher accuracy. A Global Integrity Code is generated and embedded in BR using LSB embedding technique for global tamper detection. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.03 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0019 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed scheme is highly secure and the computational cost is also low. The average embedding and extraction time (including embedding, encryption and decryption, extraction process respectively) is 0.88 s and 0.83 s. It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme’s effectiveness.

This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact is a random variable, due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to characterize the risk associated with the attack. We then consider the problem of allocating security measures to the set of actuators to minimize the risk. We assume that there are only a limited number of security measures available. Under this constraint, we observe that the allocation problem is a mixed-integer optimization problem. Thus we use relaxation techniques to approximate the security allocation problem into a Semi-Definite Program (SDP). We also compare our allocation method $(i)$ across different risk measures: the worst-case measure, the average (nominal) measure, and $(ii)$ across different search algorithms: the exhaustive and the greedy search algorithms. We depict the efficacy of our approach through numerical examples.

This paper presents the application of a community battery energy storage system (CBESS)-integrated microgrid (MG) in a blockchain-enabled local energy market (LEM). The proposed LEM balances the community energy requirement while facilitating frequent peer-to-peer (P2P) energy transactions between several energy users in the presence of both energy supplier and energy operator. The architecture is formulated by taking a number of local market and network constraints, that include residential battery energy storage system (RBESS) constraints; CBESS constraints; P2P traded price constraints; P2P traded power constraints; margin constraints of the stakeholders; power grid export and import constraints; and network energy balance constraints, so as to not only incentivise energy users but also reduce import/export from/to power grid while keeping the margins of energy supplier and energy operator unaffected. Different types of transactions data including energy users’ P2P pricing bids and P2P traded energy volume are also stored in the blockchain database. Further, the developed LEM framework is also validated through a case study executed on an actual Australian power grid network, comprising 260 residential energy users; two energy suppliers; an energy operator; and a CBESS, and the performance of the proposed P2P trading-based LEM strategy is compared with the existing business-as-usual (BAU) that directs energy users to buy/sell energy at the time-of-use (ToU)/ feed-in-tariff (FiT) rate. The extensive and comparative simulation results confirm the superior performance of the proposed LEM mechanism in terms of minimising energy users’ electricity bill; lowering power grid import and export; and retaining margins of energy suppliers and the energy operator; and thus, emphasise its application suitability in the current electricity market.

With the nature-powered quantum revolution gaining momentum, Quantum Computation and Quantum Information are all set to transform the landscape of all areas of science and technology. Quantum Cryptography is one such field which has witnessed tremendous progress in theory as well as in its practical implementation. Quantum Key Distribution is one of its integral parts and it necessarily requires the usage of an information reconciliation protocol to correct the errors present in the key bits transmitted via the physical system involved. Cascade is one such protocol which enjoys popularity among such system developers due to its simplicity and high error-correction efficiency. In this work, we identify, improve, interpret, and implement key optimizations from a practical viewpoint and give an algorithmic implementation of the Cascade protocol which would serve to increase its overall efficiency without changing its foundational structure. We also dig deep into the math and physical interpretation behind the values and expressions of key parameters and quantities of Cascade, which effectively make use of the cascade of errors unearthed by it so as to not only ensure an exponentially decreasing probability of any errors in the key bits with the passes of Cascade but also ensure that only the minimum possible information is leaked to a potential eavesdropper. Finally, we also give a visual walk-through of Cascade in action and support it with explanations at every step. By doing so, we aim to equip researchers and developers with an insight driven error correction tool which is sure to be useful in practice.

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

The recent interest in using visible light as a means of communication has opened up possibilities for using visible light for other applications as well, such as indoor positioning. Visible light offers higher bandwidth, immunity from electromagnetic radiation, and most importantly it can be seamlessly integrated into the existing lighting infrastructure. This paper proposes a visible light based positioning model for estimating an object’s three-dimensional (3-D) parameters such as height and radius in addition to location in an indoor environment. The model is built using neural networks (NN), trained by simulating numerous multiple object scenarios in an indoor environment. It also takes into account the shadowing effects so it can be implemented in a multiple obstacle environment. The proposed algorithm has numerous applications, such as positioning assisted communication, suspicious object monitoring and surveillance in an indoor environment. The proposed model achieves a location accuracy of 5.4 cm, which could be further improved to 4 cm at the expense of extra hardware. The accuracy in measuring the height and radius of the objects using the proposed framework is observed to be 4.39 cm and 1.27 cm, respectively. In addition, we also propose a methodology to optimize the power distribution to the light-emitting-diodes (LED’s) in order to get the optimum location accuracy while maintaining the total power constraint on the system. This method could be utilized to update the power allocated to the LEDs by exploiting the current location of the objects in the room to improve communication uplink by employing visible access points (VAP’s).

Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of a distinguishable feature of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately identify the majority of samples in five publicly available leaf datasets. However, each dataset captures the images in a highly controlled environment. This paper evaluates the performance of EfficientNet and several other convolutional neural network (CNN) architectures when applied to a combination of the LeafSnap, Middle European Woody Plants 2014, Flavia, Swedish, and Folio datasets. To normalize the impact of imbalance resulting from combining the original datasets, we used oversampling, undersampling, and transfer learning techniques to construct an end-to-end CNN classifier. We placed greater emphasis on metrics appropriate for a diverse-imbalanced dataset rather than stressing high performance on any one of the original datasets. A model from EfficientNet’s family of CNN models achieved a highly accurate F-score of 0.9861 on the combined dataset.

A brain tumor is a disorder caused by the growth of abnormal brain cells. The survival rate of a patient affected with a tumor is difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role in determining the tumor site; however, manual detection is a time-consuming and challenging procedure that can cause some errors in results. The adoption of computer-assisted approaches is essential to help in overcoming these constraints. With the advancement of artificial intelligence, deep learning (DL) models are being used in medical imaging to diagnose brain tumors using MR images. In this study, a deep convolutional neural network (CNN) EfficientNet-B0 base model is fine-tuned with our proposed layers to efficiently classify and detect brain tumor images. The image enhancement techniques are used by applying various filters to enhance the quality of the images. Data augmentation methods are applied to increase the data samples for better training of our proposed model. The results show that the proposed fine-tuned state-of-the-art EfficientNet-B0 outperforms other CNN models by achieving the highest classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models, with an overall accuracy of 98.87% in terms of classification and detection. Other DL algorithms such as VGG16, InceptionV3, Xception, ResNet50, and InceptionResNetV2 are used for comparative analysis.

In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg’s algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multi-antenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.

Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible when MapReduce workloads and virtual machines (VM) are highly heterogeneous. Therefore, in this paper, we addressed these heterogeneities and proposed an efficient MapReduce scheduler to improve resource utilization by placing the right combination of the map and reduce tasks in each VM in the virtual cluster. To achieve this, we transformed the MapReduce task scheduling problem into a 2-Dimensional (2D) bin packing model and obtained an optimal schedule using the ant colony optimization (ACO) algorithm. As an added advantage, our proposed ACO based bin packing (ACO-BP) scheduler minimized the makespan for a batch of jobs. To showcase the performance improvement, we compared our proposed scheduler with three existing schedulers that work well in a heterogeneous environment. As expected, results show that ACO-BP significantly outperformed the existing schedulers while dealing with workload and VM level heterogeneities.

The optical coupling efficiency of surface plasmon resonance (SPR) based sensors is theoretically investigated from the first principle. Analytical expressions for the instantaneous electric flux lines associated with the TM modes of a three-layer dielectric-metal-dielectric (DMD) waveguide structure are calculated. It is shown that these electric flux lines appear in the form of dis-continuous loops at the metal-dielectric interfaces. A careful investigation of the electric flux loops reveals that the transverse component of the electric flux dominates at the interface, and away from the interface the longitudinal electric flux component takes over. It is hence shown that in the incident $p$ -polarized optical light which is used to excite the surface plasmon (SP) modes in SPR sensors if the transverse component of the electric field is increased then a significant increase in the input optical coupling efficiency is observed. The increased input optical coupling increases the real part of the effective index of the SP mode of the sensor yielding an increased sensitivity of the SPR sensor. Calculations are done to optimize various design parameters of an SPR sensor in a typical prism coupling Kretschmann configuration in an angle interrogation setup. It is shown that there is an increase of 64.33% per RIU in optical coupling efficiency with a decrease in the refractive index of the high index input coupling prism and an increase of 58.787% per RIU with an increase in the refractive index of the affinity layer. Enhancement of optical coupling efficiency due to the thickness of the metal film as well as the wavelength of the input optical light has also been evaluated. The corresponding improvement in the sensitivity and changes in full width at half maxima (FWHM), the figure of merit (FOM), and signal to noise ratio (SNR) of the SPR sensor are also reported.

Optical wireless communication (OWC) is one of the promising solution to enhance the capacity of the next generation cellular as well as fiber-wireless (FiWi) broadband access networks. The proposed work aims to complement an OWC system by utilizing unmanned aerial vehicles (UAV) to improve the performance of an integrated terrestrial-air-underwater (TAU) communication framework. Specifically, an integrated FiWi based UAV assisted hybrid free-space optical (FSO) and underwater wireless optical communication (UWOC) system is proposed to meet high data rate requirement between a terrestrial ground station and an autonomous underwater vehicle (AUV). The performance of the proposed scheme is compared against the conventional radio frequency (RF) based TAU communication framework. We also optimize the divergence angle of the optical beam of FSO link using a novel COgnition-based divergence angle tracking (CODAT) algorithm such that the outage probability of FSO links is minimized and the data rate requirement of AUV is also satisfied. The impact of the position of over-sea surface (OSS) relay and UAV altitude on the performance of the proposed system is also analyzed. Through the obtained results it has been shown that the proposed OWC based integrated TAU communication framework outperforms the conventional RF based TAU communication framework.

Vehicular communication systems get more and more attention with the upcoming fifth and sixth generation. Hereby, the focus lies on the development of the co-design or co-existence of communications and sensing. So called joints communications and radar sensing systems are seen as one key technology of 6G. As joint systems will have shared waveforms and hardware platforms, there is a huge benefit in cost and space which is one essential argument for the automotive industry. However, to design such a system for new applications like platooning or intersection assistance a physical layer has to be set up to represent the real physical layer properly. The proposed system design closes the lack of such a simulation tool and allows for full physical layer simulations, including e.g. the hardware non-idealities and the channel model for 77GHz. The whole signal processing chain of the physical layer is built up and will be integrated in the higher layer state-of-the-art simulation frameworks like Veins or Artery in the next step. The communications design is developed in Simulink, whereas the sensing part is discussed. The proposed communications architecture covers several transmission approaches (serial, parallel), a CDMA based spreading, a radio frequency representation, the channel (simulated in a 3D-Ray-Tracing-tool) and the receiver structure (e.g. the synchronisation or the channel estimation). Several design criteria are discussed, like the serial or parallel design architecture, the maximum ratio combining or the phase and frequency compensation method. The whole system architecture is freely available (https://doi.org/10.5281/zenodo.6482565) and in consequence, the different signal blocks and parameters can be enabled or disabled for evaluations according to future design criteria requirements.