Convolutional neural network based key generation for security of data through encryption with advanced encryption standard

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time

Comparative Analysis of Summer Discomfort Index and Thermal Sensation Vote Using Remote Sensing Data in the Summer: A Case Study of the Mediterranean Cities Seville, Barcelona, and Tetuan

As urban areas expand, the focus on improving outdoor thermal comfort intensifies. This study generated Summer Discomfort Index (SDI) maps for Seville and Barcelona (Spain), as well as Tetuan (Morocco). SDI integrates temperature and humidity for an accurate comfort assessment. Calculations involved substituting air temperature with land surface data from MODIS and incorporating humidity from weather stations, then comparing it to Thermal Sensation Votes (TSV) gathered through surveys. The objective was to assess thermal comfort levels and explore the relationship between remotely sensed SDI and residents’ reported perception. These detailed SDI maps offer crucial insights into summer thermal conditions, advancing urban climate studies and influencing urban planning, design, and well-being strategies

Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21

Land surface temperature (LST) plays a pivotal role in the dynamic exchange of energy between the Earth’s surface and the atmosphere. This research centers on the assessment of LST from satellite data acquired by the Joint Polar-orbiting Satellite System (JPSS), specifically JPSS-2/NOAA-21, employing an innovative split-window algorithm (SWA). Atmospheric water vapor content (WVC) and surface emissivity are the two main input variables in the split-window technique. Therefore, the moderate resolution transmittance code, version 4.0 (MODTRAN 4.0), was used to simulate WVC and atmospheric transmittance. The performance of the SWA was rigorously assessed against standard atmospheric conditions, revealing its capacity to achieve an LST retrieval accuracy of 1.4 Kelvin (K), even in the presence of various errors. Moreover, the LST retrieval algorithm was validated using ground truth data sets from two Australian sites, and the RMSE value was 1.71 K. The achieved results demonstrate the algorithm’s capability to provide accurate LST estimation for NOAA-21 satellite data

Estimation of Land Surface Temperature from the Joint Polar-Orbiting Satellite System Missions: JPSS-1/NOAA-20 and JPSS-2/NOAA-21

The accurate estimation of land surface temperature (LST) is a vital parameter in various fields, such as hydrology, meteorology, and surface energy balance analysis. This study focuses on the estimation of LST using data acquired from Joint Polar-Orbiting Satellite System (JPSS) satellites, specifically JPSS-1/NOAA-20 and JPSS-2/NOAA-21. The methodology for this research centers on the utilization of the split-window algorithm, a well-established and recognized technique renowned for its proficiency in extracting accurate land surface temperature (LST) values from remotely sensed data. This algorithm leverages the differential behavior of thermal infrared (TIR) radiance measured in two adjacent spectral channels to estimate LST, effectively mitigating the influence of atmospheric distortions on the acquired measurements. To establish the accuracy of the proposed approach, the coefficients of the split-window algorithm were determined using linear regression analysis, utilizing a dataset generated via extensive radiative transfer modeling. The calculated LST values were subsequently compared with LST products provided by the National Oceanic and Atmospheric Administration (NOAA). The evaluation process encompassed the computation of root mean square error (RMSE) values, offering insights into the performance of the algorithm for both JPSS-1/NOAA-20 and JPSS-2/NOAA-21 missions. LST retrieval validation with standard atmospheric simulation indicates that the JPSS-1/NOAA-20 and The JPSS-1/NOAA-21 algorithms have demonstrated an accuracy of 1.4 K in retrieval of LST with different errors. The obtained results demonstrate the potential of the split-window algorithm to effectively estimate LST from JPSS satellite data. The RMSE values, 2.05 and 1.71 for JPSS-1/NOAA-20 and JPSS-2/NOAA-21, respectively, highlight the algorithm’s capability to provide accurate LST estimates for different mission datasets. This research contributes to enhancing our understanding of land surface temperature dynamics using remote sensing technology and showcases the valuable insights that can be gained from JPSS missions in monitoring and studying Earth’s surface processes

Assessment and synergy analysis of outdoor thermal comfort and thermal infrared remote sensing for urban heat island studies

The aim of this research is to explore the potentialities and limits of the integration of remote sensing biophysical data (land surface temperature) in the outdoor thermal comfort studies. Accordingly, by examining correlations between land surface temperature and air temperature, and using respectively remote sensing satellite data MODIS and different weather stations archives alongside questionnaire surveys. Currently, the parameters of thermal comfort indices are usually calculated using the data from one, or few permanent or portable ground-based weather stations. Due to the lack of adequate distribution of weather stations, those calculations generally do not accurately represent the alteration of thermal comfort, through time and space. Nevertheless, it has been essentially proved that despite strong tendencies between in-situ measured parameters and remotely sensed ones, various elements need to be studied (e.g., location, land surface type, vegetation, and elevation). Finally, preliminary results confirm that the proposed linear approaches are providing considerable and promising performance suitable for future specific situations and studies purposes

Hardware pipelined architecture with reconfigurable key based on the AES algorithm and hamming code for the earth observation satellite application: Sentinel-2 satellite data case

Earth Observation Satellite has facilitated the study of the earth’s environment and become powerful in various applications such as: Land temperature, Land use, urban monitoring, defense, etc. Additionally, the transmission of this image captured from the EOS to the earth must be confidential and secure against illegal access, and without data corruption. However, this task remains challenging due to the space radiation environment that could affect the satellite’s hardware, and lead to corrupted data. These issues motivate our study to provide an enhanced approach to securing the satellite and ground station link. In this paper, we propose a secure implementation-based radiation-hardened Virtex-4QV FPGA. The secure implementation utilizes a combined architecture between the AES algorithm and Hamming code to ensure security. The most significant advantage of the proposed implementation is the use of the three keys 128/192/256 bits with the pipelined architecture which leads to a high throughput and a high level of security. Based on several security criteria, the suggested cryptosystem findings demonstrate the system’s effectiveness in terms of security. Therefore, this proposed solution can be used and utilized in the EOS application

Comparative analysis of human development indicators: Tanger-Tetouan-Al Hoceima region

Human development is more than a question of the accumulation of wealth, income, or economic growth. It must be human-centred. This is why concerns as necessary as respect for human rights, the reduction of social inequalities and poverty, the promotion of equal opportunities between men and women are indeed relevant. This considers human resources not only as a means of growth but, more fundamentally, as an end of growth. The demographic variable was always a serious problem to decision-makers in different countries. It is considered to be at the root of the various handicaps of development. Morocco has carried out throughout the last forty years’ population policies to improve the well-being of its citizens. To highlight regional and provincial disparities in Morocco, we are based in this work on indices of human development, namely, SDI, ASDI, MLDI using the process of the data warehouse. Finally, we have analyzed and visualized these indices with Power BI software to make a comparative analysis of TTA provinces. Findings show that Tanger-Assilah province has a great value with 0.77 of MLDI. However, Chefchaouen province has the less one with 0.56. This study has been performed to help to establish efficient decisions and making operational insights

A Modular System Based on U-Net for Automatic Building Extraction from very high-resolution satellite images

Recently, convolutional neural networks have grown in popularity in a variety of fields, such as computer vision and audio and text processing. This importance is due to the performance of this type of neural network in the state of the art, and in a wide variety of disciplines. However, the use of convolutional neural networks has not been widely used for remote sensing applications until recently. In this paper, we propose a CNN-based system capable of efficiently extracting buildings from very high-resolution satellite images, by combining the performances of the two architectures; U-Net and VGG19, which is obtained by putting two blocks in parallel based mainly on U-Net: The first block is a standard U-Net, and the second is designed by replacing the contraction path of standard U-Net with the pre-trained weights of VGG19

Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field