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21 pages, 2794 KiB  
Article
Medical Data over Sound—CardiaWhisper Concept
by Radovan Stojanović, Jovan Đurković, Mihailo Vukmirović, Blagoje Babić, Vesna Miranović and Andrej Škraba
Sensors 2025, 25(15), 4573; https://doi.org/10.3390/s25154573 - 24 Jul 2025
Viewed by 292
Abstract
Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the [...] Read more.
Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article
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19 pages, 7664 KiB  
Article
Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
by Aida Vidal-Balea, Oscar Blanco-Novoa, Paula Fraga-Lamas and Tiago M. Fernández-Caramés
Appl. Sci. 2025, 15(13), 6959; https://doi.org/10.3390/app15136959 - 20 Jun 2025
Viewed by 391
Abstract
This article presents a novel off-cloud anchor sharing framework designed to enable seamless device interoperability for Mixed Reality (MR) multi-user and multi-platform applications. The proposed framework enables local storage and synchronization of spatial anchors, offering a robust and autonomous alternative for real-time collaborative [...] Read more.
This article presents a novel off-cloud anchor sharing framework designed to enable seamless device interoperability for Mixed Reality (MR) multi-user and multi-platform applications. The proposed framework enables local storage and synchronization of spatial anchors, offering a robust and autonomous alternative for real-time collaborative experiences. Such anchors are digital reference points tied to specific positions in the physical world that allow virtual content in MR applications to remain accurately aligned to the real environment, thus being an essential tool for building collaborative MR experiences. This anchor synchronization system takes advantage of the use of local anchor storage to optimize the sharing process and to exchange the anchors only when necessary. The framework integrates Unity, Mirror and Mixed Reality Toolkit (MRTK) to support seamless interoperability between Microsoft HoloLens 2 devices and desktop computers, with the addition of external IoT interaction. As a proof of concept, a collaborative multiplayer game was developed to illustrate the multi-platform and anchor sharing capabilities of the proposed system. The experiments were performed in Local Area Network (LAN) and Wide Area Network (WAN) environments, and they highlight the importance of efficient anchor management in large-scale MR environments and demonstrate the effectiveness of the system in handling anchor transmission across varying levels of spatial complexity. Specifically, the obtained results show that the developed framework is able to obtain anchor transmission times that start around 12.7 s for the tested LAN/WAN networks and for small anchor setups, and to roughly 86.02–87.18 s for complex physical scenarios where room-sized anchors are required. Full article
(This article belongs to the Special Issue Extended Reality (XR) and User Experience (UX) Technologies)
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19 pages, 9814 KiB  
Technical Note
EGMStream Webapp: EGMS Data Downstream Solution
by Francesco Becattini, Camilla Medici, Davide Festa and Matteo Del Soldato
Geosciences 2025, 15(4), 154; https://doi.org/10.3390/geosciences15040154 - 17 Apr 2025
Viewed by 565
Abstract
The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been [...] Read more.
The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users. Full article
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17 pages, 5365 KiB  
Article
FR-IBC: Flipping and Rotation Intra Block Copy for Versatile Video Coding
by Heeji Han, Daehyeok Gwon, Jeongil Seo and Haechul Choi
Electronics 2025, 14(2), 221; https://doi.org/10.3390/electronics14020221 - 7 Jan 2025
Viewed by 702
Abstract
Screen content has become increasingly important in multimedia applications owing to the growth of remote desktops, Wi-Fi displays, and cloud computing. However, these applications generate large amounts of data, and their limited bandwidth necessitates efficient video coding. While existing video coding standards have [...] Read more.
Screen content has become increasingly important in multimedia applications owing to the growth of remote desktops, Wi-Fi displays, and cloud computing. However, these applications generate large amounts of data, and their limited bandwidth necessitates efficient video coding. While existing video coding standards have been optimized for natural videos originally captured by cameras, screen content has unique characteristics such as large homogeneous areas and repeated patterns. In this paper, we propose an enhanced intra block copy (IBC) method for screen content coding (SCC) in versatile video coding (VVC) named flipping and rotation intra block copy (FR-IBC). The proposed method improves the prediction accuracy by using flipped and rotated versions of the reference blocks as additional references. To reduce the computational complexity, hash maps of these blocks are constructed on a 4 × 4 block size basis. Moreover, we modify the block vectors and block vector predictor candidates of IBC merge and IBC advanced motion vector prediction to indicate the locations within the available reference area at all times. The experimental results show that our FR-IBC method outperforms existing SCC tools in VVC. Bjøntegaard-Delta rate gains of 0.66% and 2.30% were achieved under the All Intra and Random Access conditions for Class F, respectively, while corresponding values of 0.40% and 2.46% were achieved for Class SCC, respectively. Full article
(This article belongs to the Section Circuit and Signal Processing)
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17 pages, 801 KiB  
Article
ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
by Chanyong Lee, Sangheon Lee, Gyupin Moon, Hyunwoo Kim, Donghyeok An and Donghyun Kang
Appl. Sci. 2024, 14(21), 9717; https://doi.org/10.3390/app14219717 - 24 Oct 2024
Viewed by 1690
Abstract
Today, energy and performance efficiency have become a crucial factor in modern computing environments, such as high-end mobile devices, desktops, and enterprise servers, because data volumes in cloud datacenters increase exponentially. Unfortunately, many researchers and engineers neglect the power consumption and internal performance [...] Read more.
Today, energy and performance efficiency have become a crucial factor in modern computing environments, such as high-end mobile devices, desktops, and enterprise servers, because data volumes in cloud datacenters increase exponentially. Unfortunately, many researchers and engineers neglect the power consumption and internal performance incurred by storage devices. In this paper, we present a renewable-zoned namespace (ReZNS), an energy and performance-optimal mechanism based on emerging ZNS SSDs. Specifically, ReZNS recycles the remaining capacity of zones that are no longer used by adding a renewable concept into the mapping mechanism. We implemented a prototype of ReZNS based on NVMeVirt and performed comprehensive experiments with diverse workloads from synthetic to real-world workloads to quantitatively confirm power and performance benefits. Our evaluation results present that ReZNS improves overall performance by up to 60% and the total power consumption by up to 3% relative to the baseline on ZNS SSD. We believe ReZNS creates new opportunities to prolong the lifespan of various consumer electronics, such as TV, AV, and mobile devices, because storage devices play a crucial role in their replacement cycle. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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15 pages, 4539 KiB  
Article
Exploring a New Physical Scenario of Virtual Water Molecules in the Application of Measuring Virtual Trees Using Computational Virtual Measurement
by Zhichao Wang, Xiaoning Zhang, Xiaoyuan Zhang, Xinli Pan, Tiantian Ma, Zhongke Feng and Christiane Schmullius
Forests 2024, 15(5), 880; https://doi.org/10.3390/f15050880 - 18 May 2024
Cited by 1 | Viewed by 1250
Abstract
Our previous studies discussed the potential of measuring virtual trees using computational virtual measurement (CVM). CVM is a general methodology that employs observational techniques in lieu of mathematical processing. The advantage of CVM lies in its ability to circumvent mathematical assumptions of tree [...] Read more.
Our previous studies discussed the potential of measuring virtual trees using computational virtual measurement (CVM). CVM is a general methodology that employs observational techniques in lieu of mathematical processing. The advantage of CVM lies in its ability to circumvent mathematical assumptions of tree shapes at the algorithmic level. However, due to the current computational limitations of desktop computers, the previously developed CVM application, namely, virtual water displacement (VWD), could only act as a primary theoretical testimonial using an idealized point cloud of a tree. The key problem was that simulating a massive number of virtual water molecules (VMMs) consumed most of the computational resources. As a consequence, an unexpected empirical formula for volume calibration had to be applied to the output measurement results. Aiming to create a more realistic simulation of what occurs when water displacement is used to measure tree volume in the real world, in this study, we developed a new physical scenario for VWMs. This new scenario, namely, a flood area mechanism (FAM), employed footprints of VWMs instead of quantifying VWM counts. Under a FAM, the number of VMMs was reduced to a few from several thousands, making the empirical mathematical process (of the previously developed physical scenario of VWMs) unnecessary. For the same ideal point clouds as those used in our previous studies, the average volume overestimations were found to be 6.29% and 2.26% for three regular objects and two artificial stems, respectively. Consequently, we contend that FAM represents a closer approximation to actual water displacement methods for measuring tree volume in nature. Therefore, we anticipate that the VWD method will eventually utilize the complete tree point cloud with future advancements in computing power. It is necessary to develop methods such as VWD and more CVM applications for future applications starting now. Full article
(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)
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17 pages, 16005 KiB  
Article
A Novel and Extensible Remote Sensing Collaboration Platform: Architecture Design and Prototype Implementation
by Wenqi Gao, Ninghua Chen, Jianyu Chen, Bowen Gao, Yaochen Xu, Xuhua Weng and Xinhao Jiang
ISPRS Int. J. Geo-Inf. 2024, 13(3), 83; https://doi.org/10.3390/ijgi13030083 - 8 Mar 2024
Cited by 6 | Viewed by 2494
Abstract
Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more [...] Read more.
Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article
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35 pages, 741 KiB  
Review
Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
by Jesus Arellano-Uson, Eduardo Magaña, Daniel Morato and Mikel Izal
Appl. Sci. 2024, 14(5), 1987; https://doi.org/10.3390/app14051987 - 28 Feb 2024
Cited by 3 | Viewed by 2098
Abstract
A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the [...] Read more.
A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions. Full article
(This article belongs to the Special Issue Cloud Computing: Challenges, Application and Prospects)
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19 pages, 5585 KiB  
Article
GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time–Frequency Analysis
by Zeyu Wang and Zoltan Juhasz
Sensors 2023, 23(20), 8654; https://doi.org/10.3390/s23208654 - 23 Oct 2023
Cited by 4 | Viewed by 3428
Abstract
Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods [...] Read more.
Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time–frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000–8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations. Full article
(This article belongs to the Special Issue Computational Challenges of High-Density Biosensor Data Analysis)
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16 pages, 7117 KiB  
Article
AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology
by Siyuan Hu, Shuai Xiao, Jiachen Yang, Zuochen Zhang, Kunyu Zhang, Yong Zhu and Yubo Zhang
Sensors 2023, 23(17), 7510; https://doi.org/10.3390/s23177510 - 29 Aug 2023
Cited by 4 | Viewed by 2322
Abstract
In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, [...] Read more.
In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV’s heading direction, considering its impact on the AUV’s speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV’s navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks. Full article
(This article belongs to the Section Internet of Things)
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24 pages, 1941 KiB  
Article
Integer Arithmetic Algorithm for Fundamental Frequency Identification of Oceanic Currents
by Juan Montiel-Caminos, Nieves G. Hernandez-Gonzalez, Javier Sosa and Juan A. Montiel-Nelson
Sensors 2023, 23(14), 6549; https://doi.org/10.3390/s23146549 - 20 Jul 2023
Cited by 2 | Viewed by 1404
Abstract
Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other [...] Read more.
Underwater sensor networks play a crucial role in collecting valuable data to monitor offshore aquaculture infrastructures. The number of deployed devices not only impacts the bandwidth for a highly constrained communication environment, but also the cost of the sensor network. On the other hand, industrial and literature current meters work as raw data loggers, and most of the calculations to determine the fundamental frequencies are performed offline on a desktop computer or in the cloud. Belonging to the edge computing research area, this paper presents an algorithm to extract the fundamental frequencies of water currents in an underwater sensor network deployed in offshore aquaculture infrastructures. The target sensor node is based on a commercial ultra-low-power microcontroller. The proposed fundamental frequency identification algorithm only requires the use of an integer arithmetic unit. Our approach exploits the mathematical properties of the finite impulse response (FIR) filtering in the integer domain. The design and implementation of the presented algorithm are discussed in detail in terms of FIR tuning/coefficient selection, memory usage and variable domain for its mathematical formulation aimed at reducing the computational effort required. The approach is validated using a shallow water current model and real-world raw data from an offshore aquaculture infrastructure. The extracted frequencies have a maximum error below a 4%. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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16 pages, 7206 KiB  
Article
An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
by Vu Hien Phan, Danh Phan Hong Pham, Tran Vu Pham, Kashif Naseer Qureshi and Cuong Pham-Quoc
Future Internet 2023, 15(7), 245; https://doi.org/10.3390/fi15070245 - 18 Jul 2023
Cited by 4 | Viewed by 2662
Abstract
The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low [...] Read more.
The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m3, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system. Full article
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13 pages, 5387 KiB  
Technical Note
OpenDroneMap: Multi-Platform Performance Analysis
by Augustine-Moses Gaavwase Gbagir, Kylli Ek and Alfred Colpaert
Geographies 2023, 3(3), 446-458; https://doi.org/10.3390/geographies3030023 - 17 Jul 2023
Cited by 10 | Viewed by 5164
Abstract
This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. [...] Read more.
This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality. Full article
(This article belongs to the Special Issue Advanced Technologies in Spatial Data Collection and Analysis)
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21 pages, 11097 KiB  
Article
Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery
by Samuel Pizarro, Narcisa G. Pricope, Deyanira Figueroa, Carlos Carbajal, Miriam Quispe, Jesús Vera, Lidiana Alejandro, Lino Achallma, Izamar Gonzalez, Wilian Salazar, Hildo Loayza, Juancarlos Cruz and Carlos I. Arbizu
Remote Sens. 2023, 15(12), 3203; https://doi.org/10.3390/rs15123203 - 20 Jun 2023
Cited by 14 | Viewed by 5894
Abstract
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking [...] Read more.
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. Full article
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23 pages, 2843 KiB  
Article
Neural Attractor-Based Adaptive Key Generator with DNA-Coded Security and Privacy Framework for Multimedia Data in Cloud Environments
by Hemalatha Mahalingam, Padmapriya Velupillai Meikandan, Karuppuswamy Thenmozhi, Kawthar Mostafa Moria, Chandrasekaran Lakshmi, Nithya Chidambaram and Rengarajan Amirtharajan
Mathematics 2023, 11(8), 1769; https://doi.org/10.3390/math11081769 - 7 Apr 2023
Cited by 35 | Viewed by 2755
Abstract
Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented [...] Read more.
Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented preemptively through a host of proven cryptographical solutions. The presented work integrates adaptive key generation, neural-based confusion and non-XOR, namely DNA diffusion, which offers a more extensive and unique key, adaptive confusion and unpredictable diffusion algorithm. Only authenticated users can store this encrypted image in cloud storage. The proposed security framework uses logistics, tent maps and adaptive key generation modules. The adaptive key is generated using a multilayer and nonlinear neural network from every input plain image. The Hopfield neural network (HNN) is a recurrent temporal network that updates learning with every plain image. We have taken Amazon Web Services (AWS) and Simple Storage Service (S3) to store encrypted images. Using benchmark evolution metrics, the ability of image encryption is validated against brute force and statistical attacks, and encryption quality analysis is also made. Thus, it is proved that the proposed scheme is well suited for hosting cloud storage for secure images. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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