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With the rapid development of internet technologies and the increasing availability of large-scale data, the detection of community structures within complex networks has become a critical area of research. This paper introduces a novel community detection technique called LB-SAM (Local Beam Search with Simulated Annealing and Modularity), designed to efficiently uncover hidden community structures in large-scale social networks. LB-SAM integrates Local Beam Search (LBS) to explore the local network structure and Simulated Annealing (SA) to globally optimize modularity, enabling the detection of communities with intricate boundaries and strong internal connections. By focusing on influential nodes to form subgroups and recursively merging them based on modularity, LB-SAM provides superior scalability and robustness in both real-world and synthetic networks. Extensive experiments conducted on 12 real-world and 6 synthetic datasets demonstrate that LB-SAM consistently outperforms existing state-of-the-art algorithms, particularly in networks with unclear community structures, and scales effectively to billion-scale networks. The proposed method has wide-ranging applications in sociology, biology, marketing, and cybersecurity, offering valuable insights into the structure and dynamics of large social networks.

In today’s digital landscape, maintaining the integrity and ownership of digital content is crucial across various fields, including the critical domain of medical applications. However, existing watermarking techniques face significant challenges, such as vulnerability to attacks and limitations in capacity and robustness. To address these challenges, this work presents a novel hybrid reversible watermarking approach utilizing the Lucas number series (LNS), normalization, and squint ternary least significant bit (STLSB). Additionally, the method incorporates a lightweight encryption technique based on homomorphic binomial coefficients (LHBC) to encrypt watermark images. The encrypted image is then embedded into the cover image at the STLSB position, specifically three bits from the left-most significant bit, resulting in a watermarked image. The Present issues in watermarking medical data include ensuring the watermark’s robustness against attacks while maintaining data integrity and confidentiality, and balancing the need for traceability with the risk of compromising sensitive patient information. Experimental results show that the proposed method significantly improves embedding capacity by approximately 10% compared to existing techniques, achieving a PSNR of 52.0 dB, NCC of 1, SSIM of 0.9967, and consistently low BER, demonstrating enhanced robustness against various attacks, including JPEG compression, sharpening, resizing, and rotation. These findings highlight the method’s effectiveness for secure medical image watermarking applications, ensuring integrity and confidentiality, and extending to practical uses in digital asset management, copyright protection, and authentication systems across diverse industries.

This paper proposes a paradigm that combines edge computing and blockchain technology in the healthcare field, providing a groundbreaking method to handle, analyze, and safeguard medical data. The method effectively decreases the time delay, addresses problems related to data transmission capacity, and improves the protection of sensitive patient data by processing it locally. The blockchain network is essential to the system since it offers a secure and unchangeable record for storing processed data. The integration of this novel system guarantees that patient data, once recorded, is unable to be modified in a way that affects previous entries, thereby preserving the integrity of the data. This approach prominently incorporates the innovative Proof of Trust and Expertise (PoTE) consensus process, which utilizes the reliability and specialized knowledge of users in the network. This approach not only guarantees the secure and efficient validation of transactions, but also conforms to the essential demands of managing healthcare data. The proposed model provides efficient environment that is scalable and reliable with average block processing time of 0.25 sec, transactional throughput of 140 transactions per second (tps) and a success rate of 97% while operating over larger number of nodes in the network. The incorporation of edge computing and blockchain in healthcare is positioned to establish a novel benchmark in the management, processing, and security of medical data, consequently augmenting the overall caliber of healthcare services.

In this paper, we designed and implemented a multi-antenna configured secure millimeter-wave (mmWave) CP-less multi-user orthogonal chirp division multiplexing (OCDM) transceiver. The proposed simulated system emphasizes more applicable performance metrics for a typically assumed case of four users and a passive eavesdropper for audio data transmission. We introduce a four-dimensional hyperchaotic system-based encryption algorithm to enhance physical layer security (PLS). In addition, low-density parity check (LDPC), TURBO, ( $3, 2$ ) single parity check (SPC), and repeat and accumulate (RA) channel coding with Cholesky decomposition-based zero-forcing (CD-ZF) and minimum mean square error (MMSE) signal detection techniques for a better bit error rate (BER) were also implemented. The simulation results signify the effectiveness of the proposed system in terms of PLS enhancement with low correlation coefficients (14.62%, 7.61%, 13.61%, and 15.39% for users 1, 2, 3, and 4, respectively), an achievable secrecy rate with a low signal-to-interference and noise ratio (SINR) of the passive eavesdropper, an achievable out-of-band (OOB) power emission of 341 dB, an estimated average short-time Fourier transform (STFT) spectral power difference of 7.68 dB and estimated peak-to-average power ratios (PAPRs) ranging from 7 to 7.5 dB at a complementary cumulative distribution function (CCDF) of $1\times 10^{-3}$ for different ground transmitting channels. At an identical signal-to-noise ratio (SNR) of 17 dB, all four users achieved a bit error rate of $1\times 10^{-4}$ under RA channel coding, CD-ZF, and 16-QAM (quadrature amplitude modulation) digital modulation.

This work presents an insight into the nature of ground plane (GP) current of a microstrip patch which is truly adverse in terms of generating cross-polar (XP) fields and minimally contributing to the primary radiation. This study also uses a theoretical basis in exploring a simple solution to mitigate the high XP values, specifically over the diagonal planes (D-planes). This actually turns out to be an engineered GP by clipping off its four corners. This has been thoroughly studied for a set of representative patch geometries and experimentally verified. A consistent XP reduction by 12–13 dB over D-planes has been ensured without affecting the impedance matching or antenna gains. This eventually results in cross-polar discrimination (XPD) of the order of 25–31 dB uniformly over the full range of operating bandwidth. In addition, the design is advantageous in terms of reduction in antenna size or the deployment area. Such a simple low-cost design appears as truly improved alternative to a typical standalone microstrip radiator for practical applications.

Pest identification is a challenging task in the agricultural sector, as accurate and timely detection of pests is essential for effective pest control and crop protection. Conventional approaches to pest detection, such as entomological knowledge and manual examination, take a lot of time and are prone to human mistakes. The advent of Deep Learning (DL) techniques has revolutionized the field of computer vision, enabling automated and efficient pest recognition systems.In this research, we compared the effectiveness of many deep learning models and suggested an enhanced approach for more effective feature extraction. In the proposed approach, we have incorporated two parallel attention mechanisms in ResNet architectures and it has a significant improvement in performance. Experimental result shows that the performance accuracy obtained in ResNet50-SA, ResNet101-SA, and ResNet152-SA is 99.80%, 88.48% and 96.68%, respectively. The performance of ResNet50-SA outperforms the other state of art deep learning by a large margin. The result shows that ResNet with self-attention (SA) has a better ability to extract features and focus on the important features which increases the performance.

The Industrial Internet of Things (IIoT) is able to connect machines, analytics and people with IoT smart devices, gateway nodes and edge devices to create powerful intuitivenesses to drive smarter, faster and effective business agreements. IIoT having interconnected machines along with devices can monitor, gather, exchange, and analyze information. Since the communication among the entities in IIoT environment takes place insecurely (for instance, wireless communications and Internet), an intruder can easily tamper with the data. Moreover, physical theft of IoT smart devices provides an intruder to mount impersonation and other attacks. To handle such critical issues, in this work, we design a new private blockchain-envisioned access control scheme for Pervasive Edge Computing (PEC) in IIoT environment, called PBACS-PECIIoT. We consider the private blockchain consisting of the transactions and registration credentials of the entities related to IIoT, because the information is strictly confidential and private. The security of PBACS-PECIIoT is significantly improved due to usage of blockchain as immutability, transparency and decentralization along with protection of various potential attacks. A meticulous comparative analysis exhibits that PBACS-PECIIoT achieves greater security and more functionality features, and requires low costs for communication and computational as compared to other pertinent schemes.

Aperture-fed microstrip patch is typically known for its low cross-polarized (XP) radiations, however it stands true only over its orthogonal plane. The same, over the diagonal planes are as high as that for any other common feeds. This specific issue has been addressed and successfully resolved by proposing a technique for the first time. This introduces a pair of additional printed loops adjacent to the radiating patch. Detailed investigations leading to the design insight have been presented. A C-band rectangular patch promises as much as 7 dB suppression in the diagonal plane XP level keeping the overall performance across the principal radiation planes unchanged. This is an important step in keeping the XP levels for an aperture-fed antenna truly low thus maintaining more than 22 dB of co- to cross-polar isolation over the entire azimuth.

This paper suggests a method for active power sharing between several dispatchable and dispersed generation units in a microgrid with one or more connections to the main grid. The dispatchable power sources within the microgrid must promptly and adequately make up for natural demand changes and the intermittent nature of renewable energy resources for optimal operation. In microgrids with one or more grid connectivity, the paper presents a novel active power-sharing strategy that achieves the above-mentioned goal effectively. The suggested method’s robustness is verified under various microgrid operating circumstances. Results confirmed the proposed method’s adaptability to different operating conditions in the real world. Active power-sharing approaches are examined for a microgrid with several dispatchable distributed generation units (DDGU) operating in grid-interconnected and independent/isolated modes. Multiple grid connections at different points of the microgrid are investigated and recommended approaches are appraised. On a microgrid with one or more links to the conventional grid, this paper provides a novel and trustworthy approach for active power sharing across several DDGU. The proposed active power-sharing method is an effort to establish a feasible power-sharing technique that can be categorised for all, or most, types of interconnected microgrids. The outcomes demonstrate how the suggested technique can be adjusted to fit a range Results demonstrate how the proposed technique can be adjusted to fit a wide range of real life operational circumstances of microgrids.

This letter proposes novel approximate 4:2 compressors developed using input reordering circuits and input combination probabilities. The input reordering circuit is used to reduce the hardware complexity of the proposed designs. This letter proposes two designs of approximate 4:2 compressors. This compressor is used in designing an approximate multiplier. The proposed multiplier designs utilize less energy than the already published ones due to acceptable inaccurate output/precision, which are best suitable for image processing applications. The proposed multiplier designs MUL1, MUL2, MUL3, and MUL4 saves 22.75%, 21.95%, 11.57%, and 8.95% energy than the best of the existing design (Kong and Li, 2021). [ABSTRACT FROM AUTHOR]

Embedded-chip planar silver-elastomer interconnect technology is developed with flexible substrates and demonstrated for on-skin biophotonic sensor applications. This approach has several benefits and is also consistent with chip-thinning where the chip thickness is 100 microns and less. The key benefits from this approach arise because both the bottom and top sides are now available as flat surfaces for 3D integration of other components. It also results in the lowest electrical parasitics compared to flipchip with adhesives or printed-ramp interconnections with surface-assembled devices. Embedding of chips in flexible carriers was accomplished with direct screen-printed interconnects onto the chip pads in substrate cavities. Silver nanoflake-loaded polyurethane is utilized in the embedded-chip packages to provide the desired lower interconnect resistance and also reliability in flexible packages under deformed configurations. Viscoelastic models were utilized to model the interconnection stresses. Planar interconnects in flexible substrates are developed with conductive silver-loaded elastomer interconnects. This approach is compared to direct chip-on-flex assembly technology for reliability under bending and high-temperature storage. The embedded-chip technology is demonstrated through biophotonic sensor applications where light sources (LEDs) and photodetectors are embedded inside the package. Functional validation in bent configuration at low curvatures is shown by measuring pulse rate and muscle activity with human subjects. By extending this technology to nanowires in elastomers, further enhancement in electrical and reliability performance can be achieved.

The timely identification of plant diseases prevents the negative impact on crops. Convolutional neural network, particularly deep learning is used widely in machine vision and pattern recognition task. Researchers proposed different deep learning models in the identification of diseases in plants. However, the deep learning models require a large number of parameters, and hence the required training time is more and also difficult to implement on small devices. In this paper, we have proposed a novel deep learning model based on the inception layer and residual connection. Depthwise separable convolution is used to reduce the number of parameters. The proposed model has been trained and tested on three different plant diseases datasets. The performance accuracy obtained on plantvillage dataset is 99.39%, on the rice disease dataset is 99.66%, and on the cassava dataset is 76.59%. With fewer number of parameters, the proposed model achieves higher accuracy in comparison with the state-of-art deep learning models.

Traffic congestion is increasing in over populated urban areas, our traditional traffic management system can no longer solve this problem. However, this problem can be solved by converting our road networks into queueing networks. This paper proposes, the use of an open Jackson Queueing Model which can be equated with our existing road network to calculate the waiting time of vehicles in the network, which can be further processed to put the network into an optimized state. In this model, the waiting time depends on both arrival rate and service rate. Our implementation designates a set of service rates from a range of possible service rates for each road within the network and these designated service rates are used to calculate waiting time. As these various combinations of service rates of different roads produce multiple waiting times, we select the combination set of service rates which has the lowest set of waiting times. This set of service rates is also compared with quadratic optimization problem of the same road network to evaluate implementation’s accuracy.

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO2 gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO2 emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO2 emission.

Graphical texts in natural images play an important role in portraying information in multitudinous fields such as communication, education, and entertainment to name a few. Recognizing text in scene images is challenging due to the inherent complexity of the images. Text recognition in natural images involves script identification, which requires text localization. This is not trivial for natural scene images due to the presence of disparate foreground/background components. For scene images like movie posters, the challenge is more dominant. The challenges aggravate due to the presence of composite characteristics of posters like complex graphics background and the presence of different texts like a movie title, names of actors, producers, directors, and tagline. These texts have miscellaneous fonts, variations in colors, size, orientation, and textures. In this work, an M-EAST (modified EAST) model is proposed, which is based on the EAST (efficient and accurate scene text detector) model for text localization. A novel movie title extraction is thereafter used for separating the title from the extracted text pool. Finally, the title script was identified using a shallow convolutional neural network (SCNN)-based architecture to ensure functionality in low-resource environments. Experiments were performed on a dataset of movie-poster images of Tollywood, Bollywood, and Hollywood industries, and a highest accuracy of 99.82% was obtained. The system performed better than the reported techniques.

Although the utilizing of renewable energy sources (RESs) in microgrid (MG) offers a recognized solution to meet the increasing demand, it’s performance depend on various meteorological factors of RESs. Again, the functioning of MGs is often affected with certain industrial load dynamics which allowing them to alter the operating region and tracking function of the MGs. The above-mentioned challenges motivate us to design the ancillary voltage control design for enabling the MGs to provide adaptive transient and tracking voltage responses over the changes of various factors like weather, consumer demand, and industrial loads. Firstly, we design an intelligent adaptive control (IAC) framework made by merging with proportional-integral (PI) regulator and artificial neural network (ANN) to sustain the regulated common bus voltage over the mentioned changes. The regulated bus voltage is forwarded to operate the industrial loads via the regulation of inverter-based secondary network (SN). A study on the variation of weather condition and consumer demand is done to show the efficacy of the IAC framework. Secondly, we propose a novel fixed control structure named model reference modified fractional-order PID (MR-F0PID) regulator to maintain the high tracking response of the MG via the control of inverter associated with the SNs. The tracking competency of this fixed control framework is analyzed over the running of a few industrial loads dynamics associated with single-phase inverter based SN and results are compared with the other related existing controllers. Moreover, a mathematical analysis for mapping the stable region is completed here to track down the closed-loop stability area. As a further study, the three-phase inverter based SN associated with several three-phase industrial load is also considered with the same DC bus and analyzed to observe the competency of the proposed fixed MR-FOPID control framework.

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of n-body motion. Additional alignment cues can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid the quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing deep-learning-based and general-purpose techniques (which do not take training data) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different data.

Cyber-physical-social systems (CPSSs) epitomize an evolving paradigm, including the social, physical, and cyber world. The vital goal of CPSSs is to offer personalized, high-quality, and proactive services for the end-users. An ingenious framework for reliable services is required for CPSSs to achieve this purpose. In this regard, the cloud storage environment of cloud computing (having a great connection with the physical, cyber, and social world) requires a reliable framework for secure communication between cloud and users. Cloud storage provides various services that need scalable, cost-effective, and secure facilities of data management. Public cloud storage bound its users to maintain strict security considerations that are offered by cloud service providers. On the other hand, an opportunity for users is offered by private cloud storage to construct a controlled and self-managed model of data security. This mobile model is responsible for managing the sharing and accessing of data privately. Despite that, it induces new challenges of data security. One censorious problem is to ensure the authenticated and secure model of data-storage for accessing the data under the controlled environment of data accessibility. To tackle this challenge, many protocols have been developed. The problem is that all these protocols are unable to fulfill the required security efficiency and are susceptible to various security attacks. Recently, Tiwari et al. presented an authentication scheme for data sharing and access with a biometric feature. They claimed that their scheme resists significant security attacks. However, in this article, we show that the claim of Tiwari et al. for developing a secure scheme is not valid, and their protocol is insecure against user and server impersonation attacks. Moreover, the protocol of Tiwari et al. do not provide user anonymity. Therefore, we present an enhanced, secure, and convenient scheme for data access. Besides, in order to add the flexible distribution of data that is controlled by data-owner, our protocol provides proxy re-encryption in which the cloud server utilizes the proxy re-encryption key. Then, the data-owner generates the credential token during decryption for controlling user's accessibility. The security analysis determines that our proposed protocol resists numerous security attacks. Furthermore, performance analysis determines that our protocol has practical computation, communication, and storage costs as compared to various related protocols. Consequently, our introduced protocol achieves not only the security goals but also has performance efficiency comparable to numerous relevant protocols of cloud storage.

The Internet of Things (IoT), which enables a wide variety of embedded devices, sensors, and actuators (known as smart things) to interconnect and exchange data, is a promising network scenario for bridging physical devices and virtual objects in the cyber world. Considering the limited capacity of smart things, cloud computing has been introduced to store and process the huge amount of data collected by the IoT. The appropriate integration of cloud computing and the IoT can be regarded as the best of two worlds, simultaneously providing omnipresent sensing services and powerful processing capabilities. Undoubtedly, the cloud-assisted IoT will boost the advancement of innovative applications and services including smart cities, industrial IoT, intelligent transportation, and electronic health systems. Despite the benefits of cloud-aided IoT, it is impossible to overlook the significance of security and privacy in this kind of highly heterogeneous and interconnected system. To deal with security threats to smart devices and sensitive data, hundreds of security solutions have recently been put forward for either the cloud or IoT environments. However, a few important characteristics such as heterogeneity and scalability have not been properly considered in these solutions.

Chen et al. indicated the inner keyword guessing attack coming from the low entropy of keywords, which eliminates the semantic security of most keyword search schemes. Then, they accordingly propose the first dual-server PEKS scheme (abbreviated as DS-PEKS) and its related security models to prevent such attacks. In the DS-PEKS architecture, two non-collusive servers must corporate to execute the keyword search procedure. No individual server has the capability of determining the equivalence between keywords alone, and thus the inner keyword guessing attacks can be avoided. In this article, we found that the security models are lack of soundness and strength, so our first result is to define new stronger and sounder security models which implies all security aspects of original models. Secondly, we also propose a generic construction of DS-PEKS schemes based on IND-CCA2 secure PKE schemes. Finally, we analyze the newly proposed DS-PEKS scheme, and proof its security with the stronger models based on the IND-CCA2 security in the standard model.

We have developed a manufacturing-friendly spin-orbit torque magnetic random access memory (SOT-MRAM) technology in CMOS compatible 8-inch fab process. The proposed SOT-MRAM process technology resolves etching non-uniformity and reduction of high resistivity heavy-metal nanowire resistance issues. Besides, we present device size-dependent switching current threshold in the proposed SOT-MRAM cell structure. To realize the potential of our fabricated SOT-MRAM, wafer-level uniformity, cycling and temperature dependence SOT switching have been comprehensively investigated. Furthermore, the thermal stability factor ( ${\Delta }$ ) was calculated from temperature-dependence SOT switching to fulfill the thermal stability criteria, i.e., > 10 years of this emerging SOT-MRAM technology.