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Military Vehicle Object Detection Based on Feature Representation and Refined Localization Using Inception Recurrent Convolutional Neural Network 2

Military Vehicle Object Detection Based on Feature Represent...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Almusawi, Muntather Yadav, S. G. Shivaprasad Rahim, Abdul Aluvala, Srinivas Ramachandra, A.C. The Islamic University Najaf Iraq Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

Advanced reconnaissance technologies for military vehicle components form basis for weapons and equipment reconnaissance and tracking operations in complex environments, which is crucial to allow intelligent warfare using information. Addressing limitations of conventional detection algorithms in this domain, a novel approach termed as Military Vehicle Object Detection Method based on Inception Recurrent Convolutional Neural Network (MVODM-IRCNN) and Hierarchical Feature Representation is proposed for detecting military vehicle object. first is a comprehensive data set specially designed for military vehicles. Then it is two system improvements: hierarchical feature representation and state-of-the-art localization, using Inception Recurrent Convolutional Neural Network (IRCNN), aimed at improving detection performance. Hierarchical feature representation techniques make it easier to find good feature representation levels for different object shapes, improving accuracy of these techniques boxes. combination of materials provides a significant gain in detector performance. Finally, an exploratory study conducted on own dataset shows that MVODM-IRCNN has exceptional recognition capabilities, which proves its usefulness in military vehicle recognition applications. © 2024 IEEE.

关键词： Object detection

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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization 2

Fault Classification in Photovoltaic Arrays Using Convolutio...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Pradeep Kumar, S. Umasankar, L. Almusawi, Muntather Surekha, Pothireddy Aluvala, Srinivas Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India The Islamic University Najaf Iraq Griet Department of Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382952

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods;and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems. © 2024 IEEE.

关键词： Convolutional neural networks

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An electromagnetic view of relay time in propagation of neural signals

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Chinese Physics B 2021年第2期30卷 589-594页

作者： Jing-Jing Xu San-Jin Xu Fan Wang Sheng-Yong Xu School of Microelectronics Shandong UniversityJi'nan 250100China Department of Electronics School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China

We review the experimental and computational data about the propagation of neural signals in myelinated axons in mice,cats,rabbits,and frogs published in the past five *** contrast to the natural assumption that neural signals occur one by one in time and in space,we figure out that neural signals are highly overlapped in time between neighboring *** phenomenon was occasionally illustrated in some early reports,but seemed to have been overlooked for some *** shift in time between two successive neural signals from neighboring nodes,defined as relay timeτ,was calculated to be only 16.3μs-87.0μs,i.e.,0.8%-4.4%of the average duration of an action potential peak(roughly 2 ms).We present a clearer picture of the exact physical process about how the information transmits along a myelinated axon,rather than a whole action potential peak,what is transmitted is only a rising electric field caused by transmembrane ion *** in the paper,τrepresents the waiting time until the neighboring node senses an attenuated electric field reaching the threshold to trigger the open *** mechanisms addressed in this work have the potential to be universal,and may hold clues to revealing the exact triggering processes of voltage-gated ion channels and various brain functions.

关键词： neural signal relay propagation velocity electromagnetic field model ion channels

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Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

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Evaluating the Robustness of Neural Networks Against Adversarial Perturbations 2

Evaluating the Robustness of Neural Networks Against Adversa...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Bhat, C. Rohith Nandhini, J. Narayanasamy, S. Karthik, B. Lakineni, Prasanna Kumar Taqui, Syed Noeman Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai India Jai Shriram Engineering College Department of Ece Tirupur India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

N eural networks have impressive powers in the field of deep learning and have been used in many different applications. Nevertheless, the issue of robustness against deliberate harmful inputs, commonly referred to as adversarial perturbations, continues to be a prominent topic. The presence of these perturbations, which are frequently undetectable by human observers, has the potential to cause erroneous predictions in models, hence presenting potential hazards to crucial domains such as autonomous vehicles or medical diagnostics. The primary objective of this study is to assess the vulnerability of neural networks to adversarial perturbations, ascertain the underlying factors contributing to this susceptibility, and investigate viable solutions for mitigating such vulnerabilities. Various adversarial attack strategies were utilized on diverse neural network topologies, and their performances were assessed. The results of our study suggest that whereas deeper models exhibit enhanced classification abilities, they may not necessarily possess more resilience against hostile inputs. Additionally, it has been found that certain conventional regularization techniques might further amplify susceptibility. In order to address these problems, innovative training techniques and adjustments to the network structures are implemented, resulting in enhanced resilience against adversarial attacks. This study highlights the significance of developing neural networks that possess both accuracy and robustness, hence advancing the frontier of safe deep learning applications. © 2023 IEEE.

关键词： Adversarial Perturbations Deep Learning Neural Networks Robustness

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach 3

FPGA Implementation of Textural Feature Extractor and Distan...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Hariprasath, S. Santhi, M. Prabakar, T.N. Koushick, V. Aravind, P. Gunasekaran, T. Saranathan College of Engineering Department of Ece Trichy India Sri Sairam College of Engineering Department of Ece Bangalore Anekal India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Mattu University Department of Electrical and Computer Engineering Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798350322842

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 331-344页

作者： Miran Bezjak Ivan Stresec Branislav Kocman Stipislav Jadrijević Tajana Filipec Kanizaj Miro Antonijević Bojana Dalbelo Bašić Danko Mikulić Department of Surgery University Hospital MerkurZagreb 10000Croatia Department of Electronics MicroelectronicsComputer and Intelligent SystemsFaculty of Electrical Engineering and ComputingZagreb 10000Croatia Department of Gastroenterology University Hospital MerkurZagreb 10000Croatia Development of Software Nanobit Ltd.Zagreb 10000Croatia

BACKGROUND The growing disparity between the rising demand for liver transplantation(LT)and the limited availability of donor organs has prompted a greater reliance on older liver ***,utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger *** accounting for additional risk factors,we hypothesize that the utili-zation of older liver grafts has a relatively minor impact on both patient survival and graft *** To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor *** In the period from April 2013 to December 2018,656 adult liver transplants were performed at the University Hospital *** multivariate Cox propor-tional hazards models were developed to independently assess the significance of donor *** age was treated as a continuous *** approach involved univariate and multivariate analysis,including variable selection and assessment of interactions and ***,to exemplify the similarity of using young and old donor liver grafts,the group of 87 recipients of elderly donor liver grafts(≥75 years)was compared to a group of 124 recipients of young liver grafts(≤45 years)from the *** rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between *** Using multivariate Cox analysis,we found no statistical significance in the role of donor age within the constructed *** when retained during the entire model development,the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on *** insigni-ficance and low coefficient values suggest that donor age does not impact patient survival in our ***,there was no statistical evidence that the five developed models did not adhe

关键词： Liver transplantation Elderly donors Survival analysis Postoperative complications Cox proportional hazard models

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Improving Accuracy in Object Detection Using Region-Based Convolutional Neural Network

Improving Accuracy in Object Detection Using Region-Based Co...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, T.M Kiran Aluvala, Srinivas Mandal, Preetam Saranya, K. Karunakara Rai, B. Siddaganga Institute of Technology Department of Computer Science & Engineering Tumkur India Sr University School of Computer Science & Artificial Intelligence Warangal India Siddhartha Institute of Technology and Sciences Department of Ece Hyderabad India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The object detection is a fundamental computer vision task that involves identifying and locating objects within a video or image. This technique employed various shape patterns for suggestion to recognize the certain objects in an input data. The existing methods have drawbacks like cannot identify the small region of objects in image. In this research, the Region-based Convolutional Neural Network (RCNN) is proposed for object detection. The dataset utilized for object detection is Caltech101 dataset and it is pre-processed by normalization and LAB color transformation to enhance the image quality. The preprocessed images are given into ResNet-50 based feature extraction and RCNN is utilized for classification. The performance of RCNN model is estimated through the performance measures of accuracy, precision, recall, specificity and f1-score. The RCNN attained high accuracy of 95.78%, precision of 94.81%, recall of 94.27%, specificity of 93.65% and f1-score of 92.49% which is comparatively superior than other existing methods like Histogram of Oriented Gradient (HOG) and Local Ternary Pattern (LTP), Hierarchical Max-poling (HMAX) and Fuzzy C-means. © 2023 IEEE.

关键词： Deep Learning LAB color transformation Object Detection Region-based Convolutional Neural Network ResNet-50

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Power Domain Multiplexing Waveform for 5G Wireless Networks

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computers, Materials & Continua 2022年第1期70卷 2083-2095页

作者： Korhan Cengiz Imran Baig Sumit Chakravarty Arun Kumar Mahmoud A.Albreem Mohammed H.Alsharif Peerapong Uthansakul Jamel Nebhen Ayman A.Aly Department of Electrical-Electronics Engineering Trakya University22030EdirneTurkey Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Sultanate of Oman Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA Department of ECE JECRC UniversityJaipur303905India Department of Electronics and Communication Engineering A’Sharqiyah UniversityIbra400Oman Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006Korea School of Telecommunication Engineering Suranaree University of TechnologyNakhonRatchasimaThailand Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia

Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented *** employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency ***,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless ***,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high ***,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)*** the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned *** simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional ***,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC *** key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting ***,a huge count of mobile devices could be supported under specific ***-UFMC can be regarded as the most effective applications for 5G Mobile andWireless ***,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.

关键词： NOMA-UFMC 5G PAPR BER DHT-NOMA-UFMC

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A Finite Element Technique for Approximately Solving for the Electromagnetic Field Within a Cavity Resonator 3rd

A Finite Element Technique for Approximately Solving for t...

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3rd International Conference on Computational electronics for Wireless Communications, ICCWC 2023

作者： Vaish, Arti Parthasarathy, Harish Dua, Rakhi Department of Computer Science and Engineering School of Engineering and Technology O P Jindal University CG Raigarh Punjipathra India Electronics and Communication Division Netaji Subhas University of Technology Dwarka New Delhi India ECE Department KR Manglam University Gurugram India

ISBN: (纸本)9789819719426

This paper develops a four space-time dimensional finite element approach for approximately calculating the electromagnetic and Dirac fields within a 3-D cavity resonator taking into account the interaction between the two fields. The idea is to partition the four-dimensional space-time region consisting of the 3-D cavity and the finite time duration over which the fields are to be determined into four-dimensional simplices. Each simplex is defined by five vertex points in four-dimensional space-time and the fields within each such simplex are expressed as affine linear functions of the four simplex coordinates so that the values of the field at the vertices of the simplex coincides with the field value at that vertex. We then substitute these affine linear functions into the action functional of the electromagnetic field interacting with the Dirac field thereby yielding a quadratic function of the simplex vertex field values corresponding to the free field action plus cubic function of the vertex fields corresponding to the interaction action between the Dirac current and the electromagnetic field. By summing up this action over all the simplices into which we have partitioned the space-time region, we obtain a quadratic-cubic function of the field values at all the vertices of the different simplices. The problem of approximately calculating the fields within the cavity then amounts to minimizing this function of a finite number of complex variables corresponding to the field values at the vertices of the simplices. This optimization can be carried out using for example a gradient search algorithm or using perturbation theory for obtaining solutions to a system of nonlinear algebraic equations when the nonlinearity is small. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Perturbation techniques

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