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Two simple memristive maps with adaptive energy regulation and digital signal process verification

Journal of Zhejiang University-science A(Applied Physics & engineering) 2024年第5期25卷 382-394页

作者： Feifei YANG Lujie REN Jun MA Zhigang ZHU College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Information Science and Engineering Dalian Polytechnic UniversityDalian 116034China

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms,while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy *** involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive *** this study,two kinds of memristors are used to couple a capacitor or an inductor,along with a nonlinear resistor,to build different neural *** corresponding circuit equations are derived to develop two different types of memristive oscillators,which are further converted into two kinds of memristive maps after linear *** Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive *** Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and *** two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation,and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps.A digital signal process(DSP)platform is used to verify these *** scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.

关键词： Hamilton energy Discrete memristor Self-adaptive regulation Digital signal process(DSP)implementation

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An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

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Multimedia Tools and Applications 2024年 1-23页

作者： Patel, Jaideep Chandel, Sonal Telang Sonker, Abhilash Department of Computer Science Engineering Sagar Institute of Research and Technology Bhopal India Department of Computer Science Engineering Indian Institute of Information Technology Bhopal India Department of Information Technology Madhav Institute of Technology & Science Gwalior India

Crop yield Prediction based on environmental, soil, water, and crop parameters has been an active area of research in agriculture. Many studies have shown that these parameters can have a significant impact on crop yields, and that accurate predictions of crop yields can be made by considering them together. Despite the progress made in crop yield prediction, several challenges still remain, such as the lack of high-quality and diverse data, the high cost of technology, and the lack of domain knowledge for certain crops and regions. Thus, Crop yield forecasting models can reasonably estimate yields, but better yield forecasting performance is still desirable. Thus, in order to enhance the accuracy of crop yield prediction in IoT, a novel optimization driven deep belief neural network model is developed in this study. IoT sensors can collect data on various environmental factors such as temperature, humidity, soil moisture, as well as crop parameters such as growth stage and leaf area index. These data can then be used to train a proposed deep learning-based model, namely an optimization driven deep belief neural network (DBNN) along with an effective crocodile optimization algorithm, to predict crop yields. To improve the performance of DBNN, adaptive crocodile optimization (ACO) algorithm is used. Using this algorithm, weight parameters of DBNN are chosen optimally. Results of the article depict that the proposed model achieve accuracy of 97.26%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart agriculture

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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A Review of Image Steganography Based on Multiple Hashing Algorithm

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computers, Materials & Continua 2024年第8期80卷 2463-2494页

作者： Abdullah Alenizi Mohammad Sajid Mohammadi Ahmad A.Al-Hajji Arshiya Sajid Ansari Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56312Saudi Arabia

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover *** ancient times to the present,the security of secret or vital information has always been a significant *** development of secure communication methods that keep recipient-only data transmissions secret has always been an area of ***,several approaches,including steganography,have been developed by researchers over time to enable safe data *** this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,*** have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)*** this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this *** encoding the data and embedding it into a carry image,this review verifies that it has been ***,embedded text in photos conveys crucial signals about the *** review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data *** the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.

关键词： Image steganography multiple hashing algorithms Hash-LSB approach RSA algorithm discrete cosine transform(DCT)algorithm blowfish algorithm

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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

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computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Optimizing IoT Network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Department of Computer Science & Engineering Bangladesh Virginia Tech Department of Electrical and Computer Engineering United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350356717

Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural Network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT Networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in Network Intrusion Detection. © 2024 IEEE.

关键词： Network intrusion

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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