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Boosting Resolution of VHR Remote Sensing Images using CNN

Boosting Resolution of VHR Remote Sensing Images using CNN

引用

Technologies for Defense and Security (TechDefense), IEEE international workshop on

作者： Matteo Ciotola Giovanni Poggi Giuseppe Scarpa DIETI University of Naples Federico II Naples Italy Department of Engineering University of Naples Parthenope Naples Italy

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

Remote sensing is increasingly pivotal for security and defense, supporting critical decision-making processes. Achieving high-resolution imagery is essential to enhance the accuracy and reliability of these applications. Pansharpening, a technique that fuses high-resolution panchromatic images with lower-resolution multispectral images to produce high-resolution multispectral data, facilitates more effective ground element detection. Traditional pansharpening algorithms, however, often struggle with local misalignments between panchromatic and multispectral images, leading to artifacts and compromised spectral and spatial fidelity. This paper introduces a novel pansharpening algorithm that mitigates these challenges through an advanced local coregistration technique. By automatically improving the alignment of both inter-and intra-band components during the pansharpening process, our method ensures a more precise and accurate fusion. Additionally, our approach incorporates a super-resolution technique that enhances the resolution of multispectral data up to 15 cm. Experimental evaluations using WorldView-3 satellite images demonstrate the effectiveness of our approach, showcasing significant improvements in visual quality and opening new possibilities for advanced remote sensing applications.

关键词： Visualization Accuracy Superresolution Pansharpening Satellite images Security Time factors Reliability Remote sensing optimization

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Design and technology solutions for high-efficiency high-speed motors

Design and technology solutions for high-efficiency high-spe...

引用

international Conference on Electrical Machines, ICEM

作者： F. Luise A. Tessarolo S. Pieri P. Raffin M. Di Chiara F. Agnolet M. Scalabrin Motor Generator and Drives Research and Development Ansaldo Sistemi Industriali S.p.A. Monfalcone Italy Industrial Engineering and Information Technology Department University of Trieste Italy

Over the last few decades an increasing sensitivity has been spreading towards energy saving technologies. In particular, as far as electric drives are concerned, a strong demand for high efficiency motors has been raising in many industry sectors. This paper describes the development of an innovative high-efficiency solution for highspeed motors targeted for industrial turbomachinery applications in the 500-1000 kW power range at 10000-15000 rpm The technology presented features a slotless stator and a magnetically-levitated Halbach permanent-magnet rotor. A full-scale motor prototype is being manufactured for the assessment of the proposed technology. Design optimization techniques, based on genetic algorithms and multi-disciplinary analysis tools, have been employed in the prototype design stage to maximize motor efficiency and performance figures. A multilevel cascaded-cell converter with optimized voltage output is under parallel development for motor supply. Prototype full-power and full-speed qualification tests are planned to take place within the current year to demonstrate the effectiveness of the design and technology presented in this paper.

关键词： Rotors Permanent magnet motors Prototypes Magnetic levitation optimization Stator windings

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Set-based particle swarm optimization for mapping and scheduling tasks on heterogeneous embedded systems

Set-based particle swarm optimization for mapping and schedu...

引用

international workshop on Advanced Computational Intelligence (IWACI)

作者： Xiao-Xiao Xu Xiao-Min Hu Wei-Neng Chen Yun Li Ministry of Education Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Guangzhou China Sun Yat-Sen University Guangzhou Guangdong CN School of Engineering University of Glasgow Glasgow U.K.

Modern heterogeneous multiprocessor embedded platforms is important for the high volume markets that have strict performance. However, it presents many challenges that need to be addressed in order to be efficiently utilized for multitask applications. Since mapping and scheduling problems for multi processors belong to the classic of NP-Complete problems, common methods used to solve this kind of problem usually fail. In this paper, we present an algorithm based on the meta-heuristic optimization technique, set-based discrete particle swarm optimization (S-PSO), which efficiently solves scheduling and mapping problems on the target platform. This algorithm can simultaneously addressed the mapping and scheduling problems on a complex and heterogeneous MPSoC and it has better performance than other algorithms in dealing with large scale problems. This algorithm also reduces the execution time of the application by exploring various solutions for mapping and scheduling of tasks and communications. We compare our approach with other heuristics, Ant Colony optimization (ACO), on the performance to reach the optimum value and on the potential to explore the target platform. The results show that our approach performs better than other heuristics.

关键词： Scheduling optimization Processor scheduling Algorithm design and analysis Program processors Particle swarm optimization Data transfer

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Trace Analysis of Electric Micromobility and its Application for City Sensing

Trace Analysis of Electric Micromobility and its Application...

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international workshop/Symposium on Distributed Simulation and Real-Time applications (DS-RT)

作者： Alfonso Esposito Leonardo Ciabattini Luca Sciullo Marco Montanari Luciano Bononi Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Electric micromobility is increasingly being adopted as a urban transportation means. This emerging class of vehicles, thanks to its onboard hardware and embedded sensors, can be used for gathering IoT data across cities or for producing valuable insights on users’ driving behavior. However, the limited availability of public datasets on e-mobility poses a significant challenge for research, restricting the potential for improving the modeling and optimization of city-scale mobility systems. In this paper, we present an in-depth analysis of electric micromobility using GPS traces from three Italian cities. Furthermore, we identify significant spatio-temporal patterns and develop algorithms to reconstruct rental trips and model user demands, enabling the generation of synthetic vehicular traces for various urban scenarios and arbitrary number of vehicles. Our findings reveal distinct temporal patterns influenced by seasons and time of day, and spatial patterns showing varying distances covered by different vehicle types. Notably, our simulations for the city of Bologna demonstrate the potential of using electric micromobility vehicles as mobile sensors for city-sensing applications, providing extensive urban coverage with minimal units.

关键词： Analytical models Electric potential Urban areas Transportation Real-time systems Hardware Sensors optimization Micromobility Global Positioning System

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SI-based scheduling of scientific experiments on Clouds

SI-based scheduling of scientific experiments on Clouds

引用

IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Elina Pacini Cristian Mateos Carlos García Garino Universidad Nacional de Cuyo Mendoza AR ISISTAN-CONICET Tandil Buenos Aires Argentina

Scientists and engineers usually require huge amounts of computing power for performing their experiments. Precisely, Parameter Sweep Experiments (PSE) allow these kind of users to perform simulations by running the same scientific code with different input data, which results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. We describe two Cloud schedulers based on two popular swarm intelligence (SI) techniques, namely ant colony optimization (ACO) and particle swarm optimization (PSO), to allocate virtual machines (VM) to physical Cloud resources. The main performance metrics to study are the number of serviced users by the Cloud -i.e., the number of Cloud users that the scheduler is able to successfully serve- and the total number of created VMs, in dynamic (non-batch) scheduling scenarios. Simulated experiments performed by using CloudSim and real PSE job data suggest that our schedulers, through a weighted metric, perform competitively with respect to the number of serviced users and achieve an effective assignment of VMs compared to a scheduler based on Genetic algorithms.

关键词： Measurement Silicon Processor scheduling Particle swarm optimization Dynamic scheduling Insects

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A Program Behavior Study of Block Cryptography algorithms on GPGPU

A Program Behavior Study of Block Cryptography Algorithms on...

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Japan-China Joint workshop on Frontier of Computer Science and Technology (FCST)

作者： Gu Liu Hong An Wenting Han Guang Xu Ping Yao Mu Xu Xiurui Hao Yaobin Wang Department of Computer Science and Technology University of Science and Technology Hefei China Key Laboratory of Computer System and Architecture Chinese Academy and Sciences Beijing China

Recently many studies have been made to map cryptography algorithms onto graphics processors (GPU), and gained great performances. This paper does not focus on the performance of a specific program exploited by using all kinds of optimization methods algorithmically, but the intrinsic reason which lies in GPU architectural features for this performance improvement. Thus we present a study of several block encryption algorithms(AES, TRI-DES, RC5, TWOFISH and the chained block cipher formed by their combinations) processing on GPU using CUDA. We introduce our CUDA implementations, and investigate the program behavioral characteristics and their impacts on the performance in four aspects. We find that the number of threads used by a CUDA program can affect the overall performance fundamentally. Many block encryption algorithms can benefit from the shared memory if the capacity is large enough to hold the lookup tables. The data stored in device memory should be organized purposely to avoid performance degradation. Besides, the communication between host and device may turn out to be the bottleneck of a program. Through these analyses we hope to find out an effective way to optimize a CUDA program, as well as to reveal some desirable architectural features to support block encryption applications better.

关键词： Cryptography Computer architecture Acceleration Runtime Computer science Parallel processing Hardware Laboratories Computer graphics optimization methods

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Predictor set optimization for collaborative filtering

Predictor set optimization for collaborative filtering

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Orsolya Horváth Gábor Takács Széchenyi István University Gyor Hungary

One of the most efficient approaches to create a recommender system is collaborative filtering (CF). CF does not require metadata about users and items, but only interactions between users and items (e.g. ratings), therefore it can be applied in many problem domains. Experience shows that for achieving high accuracy, it is worthwhile to use a blended solution, consisting of many predictors. This paper presents an algorithm for constructing a set of CF predictors so that the overall accuracy of the set is high. The algorithm was tested on the Netflix Prize dataset that contains 100 million ratings.

关键词： Prediction algorithms Probes Collaboration Training Recommender systems USA Councils

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Improved Seagull optimization Algorithm to Optimize Neural Networks with Gated Recurrent Units for Network Intrusion Detection

Improved Seagull Optimization Algorithm to Optimize Neural N...

引用

IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Sen Ma Chunzhi Wang Aijun Liu Yucheng Zhang Junfang Wang Yuguang Chang Jie Yang Hubei University of Technology Wuhan China Wuhan Fiberhome Technical Services Co. Ltd. Wuhan Hubei China

ISBN: (纸本)9781665426060

In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved Seagull optimization Algorithm (ISOA) to optimize GRU parameters has been proposed. The ISOA is proposed to address the shortcomings of the Seagull optimization Algorithm (SOA) of low convergence accuracy and slow convergence speed in dealing with global optimization problems. The algorithm uses an opposition-based learning method instead of a random method to initialize the population so that the population has a better diversity. To balance the overall and local search capability of the algorithm, in this paper, we design a special factor update formula that converges nonlinearly with the number of iterations, allowing the algorithm to better jump out of locally optimal solutions and speed up convergence. The improved algorithm is used to obtain better parameters and uses them to set the parameters of the GRU to build a better-performing classification model. Multiple UCI datasets are used and compared with other methods and finally validated using the NSL-KDD datasets for network intrusion detection. The experimental results show that compared to the performance of long short-term memory(LSTM) units, multilayer perceptron(MLP), and the traditional neural network with GRU, the classification accuracy of the method with ISOA for optimizing GRU is improved on each dataset, which helps to improve the classification performance in network intrusion detection.

关键词： Learning systems Sociology Neural networks Network intrusion detection Logic gates Nonhomogeneous media Classification algorithms

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A New Evolutionary Computation Framework for Privacy-Preserving optimization

A New Evolutionary Computation Framework for Privacy-Preserv...

引用

international workshop on Advanced Computational Intelligence (IWACI)

作者： Zhi-Hui Zhan Sheng-Hao Wu Jun Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Chaoyang University of Technology Taichung Taiwan Victoria University Melbourne Australia

Evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the “survival of the fittest” principle in natural selection. However, in a real-world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RCF) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RCF-based privacy-preserving PSO and DE can solve the PPOP without performance loss.

关键词： Privacy Processor scheduling Neural networks Linear programming Scheduling Cryptography Artificial intelligence

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Efficient Classification of Motor Imagery using Particle Swarm optimization-based Neural Network for IoT applications

Efficient Classification of Motor Imagery using Particle Swa...

引用

workshop on Metrology for Industry 4.0 and IoT

作者： Oluwagbenga Paul Idowu Oluwarotimi Williams Samuel Xiangxin Li Mojisola Grace Asogbon Peng Fang Guanglin Li Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen China

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

The Internet of Things (IoT) has found wide applications in human endeavors, and has recently drawn massive attention in the field of cognitive science by connecting the Brain-Computer Interface (BCI) to the cloud. The integration of EEG-based BCI with internet is rapidly developing due to its non-invasiveness, and the benefits are inexhaustible such as control of assistive robots, telemedicine and telehealth for secured monitoring. However, some challenges exist which would prevent the application of BCIs, including the accurately predicting movement intention from EEG signals, computational complexity while preprocessing EEG tasks, and the problem of overfitting. Several algorithms have been used to address these challenges, but they are still limited to be applied efficiently on the BCI-based IoT. In this paper, in order to tackle the above mentioned issues, we proposed an extended Particle Swarm optimization (PSO)-based neural network (NN) which is able to provide a seamless interaction between the BCI and IoT devices. In the experiments, we designed a BCI system that captured relevant EEG information by means of the PSO, and then projected the features into a neural network system for training. The experimental results demonstrated the feasibility of the proposed PSO-based NN technique in classifying the motor imagery (MI) tasks with an accuracy of 98.9%. Some possible improvement in future work was also suggested.

关键词： EEG-based BCI MI IoT PSO_NN Particle swarm optimization Telerehabilitation Neural network Electroencephalography images Imagery (Psychotherapy) Inexhaustible Cognitive Science Internets Internet of things Motor parametric subharmonic oscillator

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