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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Chen, Guanxu Wang, Gang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

In this paper, we address the underwater localization problem with isogradient sound speed profile (SSP) using time-of-arrival (TOA) measurements. The sound speed is related to the unknown source depth in the isogradient SSP model, which is highly nonlinear. Owing to this nature of the isogradient SSP model, the maximum likelihood (ML) problem for estimating the source position is non-convex such that local convergence may occur without sufficiently good initialization. To address this difficulty, we first approximate the isogradient SSP model, and then propose a closed-form two-step weighted least squares (TSWLS) method for estimating the source position based on the approximate model. The solution method is accurate since the approximate model takes the source depth into consideration, and can be used to solve the ML problem as an initialization. Moreover, the proposed TSWLS solution is simple and computationally attractive since it is in closed form. Simulation results show that the proposed TSWLS solution is sufficiently accurate and can be used as the initial estimate for solving the non-convex ML problem, such that the mean square error (MSE) of the ML solution can reach the Cramér-Rao lower bound (CRLB). Index Terms-Underwater localization, time-of-arrival, isogradient sound speed profile, two-step weighted least squares. © 2024 IEEE.

关键词： Mean square error

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Genetic algorithm for optimizing meteorological sensors placement using airborne LiDAR data 24

Genetic algorithm for optimizing meteorological sensors plac...

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24th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2024

作者： Lukač, Niko Kavran, Domen Ovčjak, Monika Ferk Bizjak, Marko Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

ISBN: (纸本)9798350353433

This paper presents a novel method for optimizing meteorological sensor placement over large geographic areas, leveraging genetic algorithm (GA) and classified Light Detection And Ranging (LiDAR) data. The airborne LiDAR data provides detailed surface information necessary for finding the optimal locations for sensor placement. By considering semantic segmentation of surface, relative Sky View Factor (SVF), and surface slope, the method significantly reduces the search space to find suitable locations for sensors placement. The considered GA then uniformly and optimally positions a user-specified number of sensors, guided by optimization constraints within the fitness function. This approach enhances dense meteorological data collection efficiency, ensuring better data quality and coverage in areas such as weather forecasting, climate studies, and pollution monitoring. It reduces redundancy and deployment costs while maximizing sensors exposure to environmental physical processes such as solar radiation and wind. © 2024 IEEE.

关键词： Semantic Segmentation

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Optimizing Diabetes Prediction using Machine Learning with Data Deviation 6

Optimizing Diabetes Prediction using Machine Learning with D...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Triandi, Budi Tanti, Lili Puspasari, Ratih Elhiyas, Mas Ayoe Marwan Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia

ISBN: (纸本)9798331539603

Diabetes mellitus is a chronic disease that affects global health with an increasing prevalence rate. Early prediction can provide opportunities for earlier intervention and better management of health conditions. This study explores various classification techniques to analyze data sets that include potential risk factors such as family history, body mass index (BMI), blood pressure, and blood sugar levels. This approach uses several machine learning algorithms such as Decision Trees, Gradient Boosting, support vector machines (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Random Forest (RF) to identify patterns related to the development of diabetes. In this study, we have carried out a comparison of several machine learning (ML) algorithms, aiming to classify the "No Diabetes" or "Diabetes" class by considering several parameters such as, Age, gender, polyuria, polydipsia, sudden_weight_reduction, weakness, polyphagia, genital_thrush., visual_blurry, itching, irritability, delayed_healing, partial_paresis, muscle_stiffness, alopecia, obesity and classes collected in one dataset. The ML model is optimized by normalizing standard deviation data and 20-fold cross validation to get the best model and to determine the log loss or accuracy matrix and its performance. The results show that the model built using Gradient Boosting and SVM gave the best accuracy results on the entire test set, the Decision Trees, Random Forest and k-NN models also gave good results, while Naïve Bayes proved to be the least good in terms of accuracy results. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving TRISAT-R Satellite Attitude Prediction by Incorporating a High-Resolution Optical Sensor to Kalman Filtering 15

Improving TRISAT-R Satellite Attitude Prediction by Incorpor...

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15th International Conference Elektro, ELEKTRO 2024

作者： Kramberger, Iztok Kosanic, Nejc University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350372359

When flying a satellite, various operations on-board the satellite require determining quite a precise orientation to properly function, such as Earth or Sun observation, downlink communication and similar. This results in satellite attitude estimation posing a crucial problem to be solved, using a wide array of spacecraft control systems and sensor readings. Due to the noise in magnetic sensor measurements and drift in gyro meters, auxiliary (optical) subsystems are widely used to add reference measurements and correct the noise and the drift. Since the star-trackers, most widely used optical subsystem for attitude estimation, can be relatively large and high energy consuming, a smaller alternative is found in photodiodes and small CMOS cameras, both of which are being currently used on board TRISAT-R. Even with additional optical filters, there is error present in all measurements and error estimation is necessary. Recently the development and use of Unscented Kalman Filter (UKF) has shown greater potential for use in space related estimation, due to UKF not using classic Kalman linearization but employing a set of sigma points to calculate the systems mean and covariance more accurately. TRISAT-R use of on-board systems to tackle the problem of attitude estimation is described. UKF filtering is briefly discussed, and the simulation model for UKF of TRISAT-R orbiting is proposed. The results show an increase in accuracy of attitude prediction when using CMOS cameras for prediction instead of photodiodes, which confirms the idea that small cameras can be used for in-orbit photodiode calibration. © 2024 IEEE.

关键词： Photodiodes

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A Power Balanced Against Side Channel Attack SM4 Algorithm Based on RRAM/CMOS Hybrid Technology 9

A Power Balanced Against Side Channel Attack SM4 Algorithm B...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Wang, Pengju Zhang, Yuejun Wei, Hongshuai Zhang, Huihong Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331509453

The SM4 algorithm is now widely used to ensure the security of data transmission, but its physical implementation is still vulnerable to side channel attack. By studying the structure of SM4 algorithm, this paper proposes a scheme of SM4 algorithm against side-channel attack based on RRAM/CMOS hybrid power balancing. The scheme first studies the dynamic power balancing mechanism under the cooperative operation mode of RRAM cell and CMOS, and designs the complementary output RRAM/CMOS hybrid power balancing cell circuit by utilizing the RRAM programmable resistor states and the efficient control of CMOS logic gates;then, the leakage analysis reveals that there is a vulnerability of SM4 algorithm hardware implementation in the wheel output. To address this issue, the power balancing cell circuit is applied to the wheel output position of SM4 algorithm to decouple the correlation between power consumption and data in the output stage;finally, the power consumption curve is extracted by Spectre simulation under TSMC 65nm process and verified against correlation power analysis (CPA) attack. The experimental results demonstrate that, compared with the traditional implementation, the circuit proposed in this paper can still defend against CPA attack with 1200 power consumption curves, with more than two times higher hardware security than the traditional SM4 implementation. © 2024 IEEE.

关键词： CMOS integrated circuits

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Technical Means for Teaching 'computer Modelling and Information Technology' at Lower Secondary Education 23

Technical Means for Teaching 'Computer Modelling and Informa...

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Minchev, Dimitar Pisarova, Ivanka Burgas Free University Faculty of Computer Science and Engineering Burgas Bulgaria

ISBN: (纸本)9798350382365

This scientific article examines the importance of technological tools in teaching computer Modeling and Information Technology in lower secondary education. The focus is on integrating popular platforms such as micro:bit, Arduino, and Raspberry Pi into the learning process. The article presents pedagogical approaches and methods that use these technologies to enrich the learning environment and support the development of computer modeling and information technologies in students at lower secondary education. © 2024 IEEE.

关键词： Digital elevation model

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Ternary Memory Composed of Ferroelectric Field-Effect Transistor Based on Dynamic Logic 3

Ternary Memory Composed of Ferroelectric Field-Effect Transi...

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Dong, Zhichao Xia, Yinshui Li, Yang Yang, Zhaohui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350369151

Ternary Content Addressable Memory (TCAM) is widely used in development areas such as network routers and machine learning, which are receiving significant attention. Ferroelectric field-effect transistors (FeFETs) are among the most promising devices currently being researched in the field of advanced complementary metal-oxide-semiconductor (CMOS) technology. FeFETs also possess a unique hysteresis in the Ids-Vgs characteristics, which allows the device to function as both a switch and a non-volatile (NV) memory element. Traditional TCAM has the problems of VTH drop or search disturbance. We leverage FeFET property to construct a novel TCAM based on dynamic logic that can be integrated as efficient and compact logic elements into various levels of the memory hierarchy. Due to the slow trend of performance scaling based on Moore's Law and the fact that many computational tasks are now more data-centric than compute-centric, it is highly necessary to design efficient and compact logic elements based on emerging technologies to enhance performance and conserve energy consumption. © 2024 IEEE.

关键词： MOS devices

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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computer Modeling in engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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Evaluation of Concrete Characteristics Using Smart Machine Learning Techniques—A Review

Evaluation of Concrete Characteristics Using Smart Machine L...

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Proceedings of the Annual Conference of the Canadian Society of Civil engineering 2022

作者： Kapoor, Chinmay Popli, Navneet Kaur Sharma, Ashutosh Gupta, Rishi Faculty of Engineering and Computer Science University of Victoria VictoriaBC Canada

ISBN: (纸本)9783031340260

Concrete is one of the most commonly used materials for a wide range of construction across the world. The heterogeneity of concrete results in wide variation in its properties. How different ingredients are mixed, determines the performance of concrete, especially its compressive strength. Hence, rigorous testing of concrete in the laboratories before it is finally selected to be used for a specific type of construction is required. This process of testing requires large quantities of material, time and money. The advent of machine learning and artificial intelligence appears to be promising and significant work has been done in this field for the development of high-performance concrete. Traditional curve fitting models provide the ability to interpolate data, however, this paper evaluates the efficacy of machine learning models and different types of neural networks that have been specifically utilized to predict in terms of various factors, the compressive strength of concrete. Various algorithms like group method of data handling (GMDH) type neural networks, adaptive neuro-fuzzy inference system (ANFIS), hybrid modified firefly algorithm with artificial neural networks (MFA-ANN) and other hybrid ANN algorithms have been used to perform a comparatively analyzed review. © Canadian Society for Civil engineering 2024.

关键词： High performance concrete

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Deployment of a security solution for cloud 15

Deployment of a security solution for cloud

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15th International Conference on Communications, COMM 2024

作者： Chis, Alexandru Balan, Titus-Constantin Cotfas, Petru-Adrian Cotfas, Daniel-Tudor Transylvania University Faculty Electrical Engineering and Computer Science Braov Romania

ISBN: (纸本)9798350355956

Cloud computing offers benefits in the digital world, such as scalability and agility, but securing these environments requires a high level of knowledge and have the potential for misconfiguration. While cloud platforms offer user-friendly interfaces, ensuring robust cloud security requires a balance between user experience and technical expertise to avoid security vulnerabilities that arise from misconfiguration. This paper proposes a fundamental framework for improving the security posture while using the cloud. The framework uses a configurable building block approach to simplify migration and development efforts for cloud-based solutions. The framework focuses on Amazon Web Services (AWS) and automates deployment with infrastructure-as-code (IaC) principles, minimizing the need for deep infrastructure knowledge. This allows users to develop and deploy secure cloud solutions with built-in security tools and monitoring, minimizing the need for extensive infrastructure knowledge on the user's part. This framework aims to provide a hardened environment suited for cloud appropriation. The framework is leveraging security best practices and monitoring tools, harnessing a more secure approach to the use of public cloud environments. © 2024 IEEE.

关键词： Cloud security

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