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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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computer Systems science & engineering 2023年第7期46卷 835-851页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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Improvement of dynamic phosphene presentation for a cortical visual prosthesis is task-specific 46

Improvement of dynamic phosphene presentation for a cortical...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Haozhe Zac Wong, Yan Tat Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Monash University Department of Electrical and Computer Systems Engineering Department of Physiology ClaytonVIC3800 Australia

ISBN: (纸本)9798350371499

Cortical visual prostheses can restore vision by directly stimulating the neurons in the visual cortex. The goal of these prostheses is to elicit sufficient light perception, known as phosphenes, to represent complex scenes. However, stimulating a large number of electrodes in cortical visual prostheses can be problematic. This may result in distorted visual perception and failure to present the desired visual stimuli. Previous studies have successfully presented simple patterns, such as letters, by dynamically utilizing a single phosphene. To represent more complex scenes, we propose a new method of information presentation. This method limits the number of phosphenes elicited simultaneously while sequentially presenting the contours of a visual scene. We evaluated its effectiveness with 8 participants across four different tasks. We found that the improvement provided by this method is task-specific. Our findings underscore the importance of considering the temporal features of prosthetic vision in future designs. © 2024 IEEE.

关键词： Prosthetics

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Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

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computer Systems science & engineering 2023年第4期45卷 903-924页

作者： R.Nithya Roobaea Alroobaea Ahmed Binmahfoudh Zairi Ismael Rizman Department of Computer Science and Engineering Vivekananda College of Engineering for WomenTiruchengode637205Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia School of Electrical Engineering College of EngineeringUniversiti Teknologi MARA23000DungunTerengganuMalaysia

The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is *** order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed *** of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research *** the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this *** efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this *** utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by *** MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is *** Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during *** compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance

关键词： Sensor node clustering routing energy consumption multi-path routing

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A Low Profile Quadrifilar Helical Antenna

A Low Profile Quadrifilar Helical Antenna

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Pang, Zi-Yu Tam, Kam-Weng Huang, Guan-Long Chen, Qiwei Liu, Junxiao Zhang, Wenhai Sio, Hon-Pan Department of Electrical and Computer Engineering University of Macau China Department of Electrical and Computer Engineering University of Macau Macao Science Centre China Foshan University Foshan China Wujing Technology Zhuhai China Soochow University Suzhou China Macao Science Centre China

ISBN: (纸本)9798350364774

This work proposes a novel low-profile quadrifilar helical antenna for the BeiDou satellite navigation application. The designed antenna adopts four spring-shaped radiating arms, which can extend the current path of the radiating arm in a limited space, realizing the low-profile characteristics. The proposed antenna with a height of only 0.08λ shows good impedance matching in 1.55-1.60 GHz, fully covering the BeiDou B1 band. At 1.57 GHz, the half-power beamwidth (HPBW) of the right-hand circular polarization (RHCP) radiation patterns in the xoz and yoz planes are 119.6° and 118.5° respectively, and the corresponding 3-dB axial ratio beamwidths of which are 118.7° and 127°, respectively. The proposed quadrifilar helical antenna can be an excellent candidate for BeiDou satellite navigation due to its low profile and good circular polarization radiation performance. © 2024 IEEE.

关键词： Helical antennas

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Plastic Type Classification for Edge Devices Without Internet

Plastic Type Classification for Edge Devices Without Interne...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Sathyavani, M. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

The management of plastic waste in our ecosystem represents a significant environmental issue, necessitating effective sorting and categorization to facilitate efficient recycling practices. This study introduces a methodology utilizing deep learning for the automated identification of plastic varieties through the application of a Convolutional Neural Network (CNN) framework. The proposed method of adopting a pre-trained VGG16 to extract the features and tailors them with special layers in order to classify into seven categories that include HDPE, LDPE, Other, PET, PP, PS, and PVC. Data augmentation techniques were used to enhance the functionality of the model;image segmentation enhances object areas visibility. TensorFlow Lite was used to prepare the optimised model for its deployment to mobile platforms for real-time classifying different types of plastics on mobile devices. The optimization of mobile technology boosts the segregation of plastics at the origin, thereby allowing individuals to make informed choices in the recycling process and boosting recycling effectiveness through early differentiation. This approach generates great potential for alleviating environmental impacts through decentralized source-based plastic sorting and waste management. © 2025 IEEE.

关键词： Plastic recycling

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Deep Convolutional Neural Network with Artificial Bee Colony Optimization For Plant Disease Detection and Classification 2

Deep Convolutional Neural Network with Artificial Bee Colony...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anbazhagan, K. Eluri, Nageswara Rao Toomula, Srilatha Brindha, T.V. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Andhra Pradesh Guntur India Rbvrr Women's' College Department of Computer Science and Engineering Hyderabad India Sri Venkateshwara College of Engineering and Tecyhnology Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Plant diseases pose severe risks to agricultural production and global food security. For prompt intervention and mitigation, early and precise disease detection is crucial. Due to the development of trustworthy computer vision and machine learning technologies, automated plant disease detection systems are currently considered a potential solution to this issue. In this study, we provide a unique Deep Convolutional Neural Networks (DCNN)-based method for diagnosing plant illnesses. Our DCNN model automatically extracts pertinent features from plant photos, making it possible to classify healthy and unhealthy plants accurately. The dataset used in this study comprises a sizable collection of images with labels that show different plant species affected by various diseases. To improve its generalization and robustness to changing environmental circumstances and imaging artifacts, the proposed model is trained utilizing a mix of data augmentation via Haralick Texture. The suggested method uses a deep CNN to extract high-level information from photos of plants, allowing it to recognize subtle patterns suggestive of various diseases. The CNN architecture has been extensively crafted and adjusted to achieve the best accuracy and efficiency. The CNN's hyperparameters are optimized using the ABCO (Artificial Bee Colony Optimization) approach to increase classification accuracy further. ABCO mimics honeybee foraging to explore the hyperparameter space and find the best parameters efficiently. This improves the DCNN-ABCO performance. The findings demonstrate high accuracy, precision, and recall across various illness classes, demonstrating the model's dependability for practical applications and its capacity to identify previously unidentified disease patterns. © 2024 IEEE.

关键词： Convolutional neural networks

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Multiuser Capacity Enhancement in B5G Networks using Hybrid CDMA-NOMA Scheme

Multiuser Capacity Enhancement in B5G Networks using Hybrid ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sherien, Sabahat Sadia, Haleema Iqbal, Hafsa Ahmed Hablulmateen, Ridha Alaa Ceme Nust Department of Electrical Engineering Islamabad Pakistan School of Electrical Engineering and Computer Science Nust Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350357905

The rapid advancement of beyond fifth-generation (B5G) networks has opened a new era of high throughput demands, necessitating novel access schemes to overcome the associated challenges. Non-orthogonal multiple access (NOMA) has emerged as a promising technique, outperforming conventional orthogonal multiple access (OMA) in heterogeneous wireless networks. This paper introduces a new hybrid code division multiple access (CDMA)-NOMA scheme to improve multi-user capacity and power allocation (PA) efficiency in wireless networks. By combining the strengths of CDMA and NOMA, our proposed scheme forms NOMA clusters, each accommodating multiple users with varying channel characteristics. Intra-cluster interference is mitigated through successive interference cancellation (SIC), while inter-cluster interference is eliminated using CDMA spreading codes. We address the challenge of PA, a critical aspect of NOMA networks, by assigning distinct codes to all users within a cell. Through rigorous analysis, we evaluate the bit error rate performance of our hybrid scheme in diverse channel conditions, including the NYUSIM, AWGN, Rician, and Rayleigh channel models. The results presents an innovative solution to enhance B5G network capacity, demonstrating the potential of our hybrid CDMA-NOMA scheme to support more users efficiently while reducing SIC complexity. © 2024 IEEE.

关键词： Code division multiple access

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SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf Disease Detection 11th

SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf D...

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11th International Symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Paul, Rakesh Ghosh, Susobhan Ghosh, Mridul Obaidullah, Sk Md Roy, Kaushik Department of Computer Science Debra Thana S. K. S Mahavidyalaya Debra India Department of Computer Science Dinabandhu Mahavidyalaya Bongaon India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science West Bengal State University Barasat India

ISBN: (纸本)9789819797615

Globally, the greatest concerns of farmers are plant diseases. A considerable loss of yield has an immediate impact on the economy. Machine learning models exhibited capabilities to detect plant diseases. To enhance the generalization, flexibility, and adaptability of machine learning models, an ensemble model was introduced to combine many models and produce an appropriate and optimal predictive solution. To achieve better accuracy in deep learning models, it is necessary to have a significant amount of training time and memory resources. This is frequently utilized in engineering fields. Nowadays, the computational burden of training multiple independent networks increases with the growing number of deep learning models. To address these computational challenges, we introduced pruning and tuning-based fast, cost-effective sparse ensemble networks. The popularity of sparse architecture has increased because of its low memory requirements and faster inference rates. To identify an appropriate subset of ensemble members to build a smaller ensemble model, pruning is used to refrain from retraining the model. To achieve this, we first trained a base network. After that, we clone the parent network and drastically reduce each child’s features to generate an ensemble of members with various topologies. We soon trained each successor network for a smaller number of epochs. An empirical investigation demonstrated how well our method works on the pruned ensemble model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Deep learning Leaf disease Sparse fine-tuned ensemble

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Age of Actuated Information and Age of Actuation in a Data-Caching Energy Harvesting Actuator

Age of Actuated Information and Age of Actuation in a Data-C...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nikkhah, Ali Ephremides, Anthony Pappas, Nikolaos Linköping University Department of Computer and Information Science Linköping Sweden University of Maryland Electrical and Computer Engineering College ParkMD United States

ISBN: (纸本)9798350351255

In this paper, we extend the metric of Age of Actuation (AoA), and we propose the Age of Actuated Information (AoAI) within a discrete-time system that integrates data caching and energy harvesting (EH). AoA evaluates the timeliness of actions irrespective of the age of the information, while AoAI considers the freshness of the utilized data packet. We analytically characterize the performance of AoA and AoAI for the system at hand. Our findings show that while AoAI consistently decreases with increased data and energy packet arrival rates, AoA shows a counter-intuitive behavior, with a potential increase under limited data or energy availability. These metrics go towards the semantics of information and goal-oriented communications since they consider the timeliness of the utilized information to perform an action. © 2024 IEEE.

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