Image Processing Capabilities Research Articles

Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions.

Background Recent advancements, including image processing capabilities, present new potential applications of large language models such as ChatGPT (OpenAI), a generative pretrained transformer, in radiology. However, baseline performance of ChatGPT in radiology-related tasks is understudied. Purpose To evaluate the performance of GPT-4 with vision (GPT-4V) on radiology in-training examination questions, including those with images, to gauge the model's baseline knowledge in radiology. Materials and Methods In this prospective study, conducted between September 2023 and March 2024, the September 2023 release of GPT-4V was assessed using 386 retired questions (189 image-based and 197 text-only questions) from the American College of Radiology Diagnostic Radiology In-Training Examinations. Nine question pairs were identified as duplicates; only the first instance of each duplicate was considered in ChatGPT's assessment. A subanalysis assessed the impact of different zero-shot prompts on performance. Statistical analysis included χ2 tests of independence to ascertain whether the performance of GPT-4V varied between question types or subspecialty. The McNemar test was used to evaluate performance differences between the prompts, with Benjamin-Hochberg adjustment of the P values conducted to control the false discovery rate (FDR). A P value threshold of less than.05 denoted statistical significance. Results GPT-4V correctly answered 246 (65.3%) of the 377 unique questions, with significantly higher accuracy on text-only questions (81.5%, 159 of 195) than on image-based questions (47.8%, 87 of 182) (χ2 test, P < .001). Subanalysis revealed differences between prompts on text-based questions, where chain-of-thought prompting outperformed long instruction by 6.1% (McNemar, P = .02; FDR = 0.063), basic prompting by 6.8% (P = .009, FDR = 0.044), and the original prompting style by 8.9% (P = .001, FDR = 0.014). No differences were observed between prompts on image-based questions with P values of .27 to >.99. Conclusion While GPT-4V demonstrated a level of competence in text-based questions, it showed deficits interpreting radiologic images. © RSNA, 2024 See also the editorial by Deng in this issue.

Super-resolution radial fluctuations (SRRF) algorithm with better real-time performance

Super-resolution radial fluctuations (SRRF) is a type of statistical analysis based on fluorescence fluctuation characteristics to achieve super-resolution imaging. At present, facing the problems of slow reconstruction speed and weak real-time image processing. For dealing with these issues, parallel computing characteristics of FPGA is employed in this paper to achieve the acceleration of the algorithm. Firstly, the reconstruction process of SRRF algorithm process is split into five sub-modules, which are designed depended on the pipeline design idea. Secondly, the top-level module is designed to accomplish the sub-modules connection for executing the SRRF algorithmic process, while the reconstruction is finished by using the high-density image dataset. Finally, the single-frame image processing time is recorded and analysed in FPGA with the mainstream configuration of PC. The experimental results show that, in terms of the single frame image processing time, SRRF in FPGA is improved by 77.95% compared to PC, with real-time image processing capability under 10 ms image sampling period. In addition, the resolution-scaled Pearson coefficient (RSP) and resolution-scaled error (RSE) values of the reconstruction results in FPGA are similar to those of PC.

FANet: Fast and Accurate Robotic Grasp Detection Based on Keypoints

In practice, the real-time and accuracy of robotic grasp detection are two very important metrics. In the past, researchers had to sacrifice the real-time nature of the detection network in order to obtain higher detection accuracy. How to make the real-time and accuracy of the network co-exist is a problem worth studying. In order to solve this problem, this paper proposes a network, FANet, based on grasp keypoints, which improves the accuracy of grasp detection while ensuring the real-time performance. The key of this paper is how to quickly and accurately detect grasped keypoints. To this end, this paper proposes a local refinement module that optimizes and de-duplicates each feature of the multi-scale feature map, enabling the network to make full use of the multi-scale features. We also propose a global feature refinement module that allows the network to make better use of global features. We also propose a grasp keypoint optimization module that predicts the offset between the actual keypoints and the predicted keypoints, enabling the network to predict the keypoints more accurately. Moreover, we develop two FANets specifically for grasp detection on CPU and GPU, both of which can accomplish real-time grasp detection in real-world scenes. We complete the training and testing of FANet on the Cornell dataset and the Jacquard dataset, achieving SOTA results on the Jacquard dataset. We also test FANet on a dataset of unknown objects, all with good results. Finally, we use the FANet in grasping experiments with an actual Baxter robot and achieve an average grasping success rate of 96%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Real-time and accuracy are two very important metrics in robotic grasping detection. To achieve high accuracy, more time is often consumed for feature extraction. Similarly, in order to improve the real time performance, we need to reduce the time consumed in the feature extraction process, which may result in a drop in detection accuracy. How to coordinate the relationship between them, so as to have both, is a problem worth investigating. Current methods tend to focus on obtaining higher accuracy and they are willing to spend more time to achieve higher accuracy. But in some practical scenarios, such as on factory assembly lines, objects move fast, and the network needs to be able to detect the grasping position quickly, the real-time performance is more important, which makes some methods difficult to use. In addition, most of the current methods tend to focus on GPU-based robotic grasp detection methods, and in real-world scenarios we may not have such a powerful processing GPU available. In contrast, the CPU is an indispensable unit of the computer that we can use to process images without a high-performance GPU. However, compared to GPUs, the CPUs’ image processing capability is poor, making it difficult to achieve real-time processing. Faced with this situation, the problem of how to achieve real-time and high accuracy in a CPU-only robotic grasp detection network is worth studying, but most of the existing methods ignore this problem. To address these problems, we propose a Fast and Accurate robotic grasp detection Network (FANet), which not only enables the network to combine real-time and accuracy, but also enables real-time detection on CPU or GPU.

Particle Filter Enhanced by CNN-LSTM for Localization of Autonomous Robot in Indoor Environment

Particle filters play a crucial role in indoor localization tasks where GPS or similar sensors are unavailable, owing to their adaptability to dynamic environments and contribution to precise navigation. However, despite their effectiveness in non-Gaussian and non-linear settings, particle filters face challenges such as issues with particle initialization and recovery from scenarios like robot kidnapping. These limitations impede the complete autonomy of robots, a critical aspect of their functionality. This research presents a novel solution that integrates particle filters with a CNN-LSTM network to address these challenges. Leveraging the time-series image processing capabilities of CNN-LSTMs, this architecture aids in particle filter initialization and facilitates recovery from challenging situations. The integration enhances the overall performance and autonomy of robots, making them more efficient in indoor navigation tasks.

Scan-GPT: Bridging Visual Input and Textual Output for Question Solution

The ScanGPT project represents an innovative approach to leveraging the power of advanced language models, specifically OpenAI's GPT-3.5, in conjunction with OCR technology, to provide users with a comprehensive platform for obtaining information. In a rapidly advancing landscape of artificial intelligence, ScanGPT serves as a bridge between textual and non-textual content, allowing users to interact with multiple sources and receive clear and accurate responses. Traditional search methods often fall short when dealing with non-textual content such as images containing valuable information. ScanGPT addresses these limitations by combining OCR technology with advanced language models to deliver precise answers based on both text and image inputs. This paper presents the architecture, functionality, and methodology of ScanGPT, highlighting its role in meeting the diverse needs of users seeking information. The proposed system architecture seamlessly integrates text and image processing capabilities, leveraging existing technologies such as ChatGPT, Microsoft Azure OCR, and the OpenAI API. Through a modular design and rigorous security and privacy measures, ScanGPT ensures scalability, flexibility, and user confidentiality. The role of HTML, CSS, and JavaScript in the user interface design is explored, emphasizing the importance of intuitive interfaces and dynamic capabilities in enhancing user experience. Additionally, existing solutions and challenges in conversational AI are reviewed, providing insights into the evolving landscape of AI-powered interactions. The proposed system architecture of ScanGPT offers a robust, scalable, and flexible solution for conversational AI, enabling users to interact with AI systems using both text and image inputs. By seamlessly integrating text and image processing capabilities, ScanGPT aims to redefine the boundaries of conversational AI platforms, providing users with a comprehensive and user-friendly experience. Future scope and potential advancements in conversational AI are also discussed, highlighting opportunities for integrating additional sensory inputs, personalization, and scalability. Through ongoing improvements in ethical AI considerations and linguistic capabilities, ScanGPT aims to remain a trustworthy and globally accessible technology, fostering wider adoption and cultural inclusivity in AI-driven interactions. Overall, ScanGPT represents a significant step forward in harnessing the power of advanced language models and OCR technology to provide users with accurate, contextually relevant information from diverse sources, paving the way for innovative solutions to everyday problems in the era of artificial intelligence.

Usefulness of the large language model ChatGPT (GPT‐4) as a diagnostic tool and information source in dermatology

AbstractBackgroundThe field of artificial intelligence is rapidly evolving. As an easily accessible platform with vast user engagement, the Chat Generative Pre‐Trained Transformer (ChatGPT) holds great promise in medicine, with the latest version, GPT‐4, capable of analyzing clinical images.ObjectivesTo evaluate ChatGPT as a diagnostic tool and information source in clinical dermatology.MethodsA total of 15 clinical images were selected from the Danish web atlas, Danderm, depicting various common and rare skin conditions. The images were uploaded to ChatGPT version GPT‐4, which was prompted with ‘Please provide a description, a potential diagnosis, and treatment options for the following dermatological condition’. The generated responses were assessed by senior registrars in dermatology and consultant dermatologists in terms of accuracy, relevance, and depth (scale 1–5), and in addition, the image quality was rated (scale 0–10). Demographic and professional information about the respondents was registered.ResultsA total of 23 physicians participated in the study. The majority of the respondents were consultant dermatologists (83%), and 48% had more than 10 years of training. The overall image quality had a median rating of 10 out of 10 [interquartile range (IQR): 9–10]. The overall median rating of the ChatGPT generated responses was 2 (IQR: 1–4), while overall median ratings in terms of relevance, accuracy, and depth were 2 (IQR: 1–4), 3 (IQR: 2–4) and 2 (IQR: 1–3), respectively.ConclusionsDespite the advancements in ChatGPT, including newly added image processing capabilities, the chatbot demonstrated significant limitations in providing reliable and clinically useful responses to illustrative images of various dermatological conditions.

Data-driven exploration of swarmalators with second-order harmonics.

We explore the dynamics of a swarmalator population comprising second-order harmonics in phase interaction. A key observation in our study is the emergence of the active asynchronous state in swarmalators with second-order harmonics, mirroring findings in the one-dimensional analog of the model, accompanied by the formation of clustered states. Particularly, we observe a transition from the static asynchronous state to the active phase wave state via the active asynchronous state. We have successfully delineated and quantified the stability boundary of the active asynchronous state through a completely data-driven method. This was achieved by utilizing the enhanced image processing capabilities of convolutional neural networks, specifically, the U-Net architecture. Complementing this data-driven analysis, our study also incorporates an analytical stability of the clustered states, providing a multifaceted perspective on the system's behavior. Our investigation not only sheds light on the nuanced behavior of swarmalators under second-order harmonics, but also demonstrates the efficacy of convolutional neural networks in analyzing complex dynamical systems.

A learnable full-frequency transformer dual generative adversarial network for underwater image enhancement

Underwater applications present unique challenges such as color deviation, noise, and low contrast, which can degrade image quality. Addressing these issues, we propose a novel approach called the learnable full-frequency transformer dual generative adversarial network (LFT-DGAN). Our method comprises several key innovations. Firstly, we introduce a reversible convolution-based image decomposition technique. This method effectively separates underwater image information into low-, medium-, and high-frequency domains, enabling more thorough feature extraction. Secondly, we employ image channels and spatial similarity to construct a learnable full-frequency domain transformer. This transformer facilitates interaction between different branches of information, enhancing the overall image processing capabilities. Finally, we develop a robust dual-domain discriminator capable of learning spatial and frequency domain characteristics of underwater images. Extensive experimentation demonstrates the superiority of the LFT-DGAN method over state-of-the-art techniques across multiple underwater datasets. Our approach achieves significantly improved quality and evaluation metrics, showcasing its effectiveness in addressing the challenges posed by underwater imaging. The code can be found at https://github.com/zhengshijian1993/LFT-DGAN.

SIGNVISION: Empowering Communication Through Visual Recognition

The project "Sign Vision: Empowering Communication Through Visual Recognition" aims to develop a system that leverages advanced visual recognition technology to enhance communication for individuals with diverse needs. In today's digital era, effective communication is crucial for inclusivity and accessibility. However, traditional communication methods may pose challenges for individuals with disabilities or those facing language barriers. Sign Vision seeks to address these challenges by harnessing the power of visual recognition algorithms to interpret and translate visual information into comprehensible formats. The project will involve the development of a user-friendly interface equipped with image processing capabilities to recognize and interpret visual cues such as sign language, gestures, and symbols. Through machine learning algorithms, the system will continuously improve its recognition accuracy and adaptability to various communication styles and contexts. Additionally, Sign Vision will provide customizable features to accommodate individual preferences and accessibility requirements. Key Words: Visual recognition technology, Communication enhancement, Accessibility solutions

Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system’s response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks.

On-Board Image Enhancement on Remote Sensing Payload

CubeSats are designed to optimize applications within the strict constraints of space and power. This paper presents an On-Board Image Enhancement technique for remote sensing payloads, focusing on achieving Auto White Balance (AWB) with limited resources and enhancing the capabilities of small/microsatellites. The study introduces hardware-based techniques, including histogram adjustment, De-Bayer processing, and AWB, all tailored to minimize hardware resource consumption on CubeSats. The integrated 1U CubeSat system comprises a sensor board, an Image Data Processor (IDP) unit, and onboard computing, with a total power consumption estimated at 2.2 W. This system facilitates image capture at a resolution of 1920 × 1200 and utilizes the proposed algorithm for image enhancement on remote sensing payloads to improve the quality of images captured in low-light environments, thereby demonstrating significant advancements in satellite image processing and object-detection capabilities.

Development of Customized MIPS_32 Core Processor for Image Processing Applications

Definitely, image processing operations without advanced and expensive microprocessors consume more time, power, and larger programs. So, improving the reasonable cost of microprocessors is crucial in this situation. This paper proposes an improvement for the MIPS_32 architecture that is called a Customized MIPS_32 (CMIPS_32) to enhance the capabilities of image processing (IP) operations. The proposal aims to increase throughput by minimizing the iterative fetching of instructions required by a certain IP operation into a single customized IP instruction. The architecture of MIPS_32 was developed in two phases. Firstly, the Register File, control unit, and ALU are modified to manipulate the information related to the IP operations. Secondly, two new units, the address calculation unit and the last pixel detection unit, were proposed to determine a certain image's starting and ending addresses. Furthermore, the MIPS_32 pipeline is customized to have five to six stages depending on the intensity of operation required by a certain IP instruction to decrease the number of machine clocks and the power consumed. The proposal was implemented using the Zed-Board XC7Z020CLG484-1 FPGA. The results showed that the computation speedup increased by a factor equal to the number of standard instructions required to execute the same operation performed by one of the proposed IP instructions. The CMIPS_32 consumed less power than other models that were implemented on Spartan3-XC3S1500L, Virtex5-XC5VFX30T, Virtex6-XC6VLX75T, and Virtex6-Low-Power-XC6VLX75T by 0.0138W, 0.6468W, 1.31W, and 0.7898W, respectively. Comparing the power consumed by the proposal with the GPU proved that the CMIPS_32 consumes less than the NVIDIA-GPU-GTX980 by 63.8698W. Index Terms— FPGA, Image processing instructions, MIPS_32, Verilog.

Deep Learning-based Agricultural Pest and Disease Recognition

With the rapid development of artificial intelligence, deep learning technology has penetrated into a variety of different fields. Agricultural researchers have used deep learning's strong image and data processing capabilities to apply it to agricultural production, especially in the identification of agricultural pests and diseases, which has achieved remarkable results. The use of deep learning-based agricultural pest and disease identification technology can quickly and non-destructively identify crops in various complex environments, with high accuracy and good stability, so that farmers can quickly take effective control measures to minimize the loss of crops. This paper first explains the significance of deep learning-based agricultural pest identification technology to agricultural development, and makes a detailed analysis of the advantages and disadvantages of various pest identification technologies and their performance in pest and disease identification. Secondly, various key technologies of deep learning are introduced, including the concepts and applications of various core technologies such as data source, data preprocessing, data augmentation, network model selection, and transfer learning. Finally, based on the above research results, the problems encountered by deep learning in the identification of agricultural pests and diseases are analyzed, and its future development direction in the identification of pests and diseases is discussed.

Scaled aggregation operations over three-dimensional extended intuitionistic fuzzy index matrices

Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing.

Abstract 3533: Spatially Resolved Laser-Activated Cell Sorting (SLACS) and AI integration for enhanced pathological analysis and biomarker discovery

Abstract This study focuses on the integration of Artificial Intelligence (AI) with Spatially Resolved Laser-Activated Cell Sorting (SLACS) for enhanced pathological analysis and biomarker discovery. The primary approach involves using AI to automatically analyze slide images, identifying critical areas associated with diseases such as cancer or cells emitting specific signals. AI algorithms efficiently segment these significant regions or cellular groups, facilitating targeted analysis. Following the AI-driven segmentation, SLACS comes into play, isolating the identified cells from the slide for further examination. This precise sorting allows for the focused study of cells relevant to disease processes, enhancing the efficiency and accuracy of pathological analysis. The separated samples obtained through SLACS are then subjected to detailed analysis. This step is crucial for understanding the cellular mechanisms at play in disease states and for the identification of potential biomarkers. These biomarkers are vital for developing targeted therapeutic strategies and advancing personalized medicine. By combining AI's advanced image processing and pattern recognition capabilities with the precision of SLACS, this integrated approach significantly improves the identification and analysis of disease-related cellular characteristics. It streamlines the process of isolating and studying specific cell populations, contributing to a deeper understanding of disease mechanisms and progression. In conclusion, the synergistic application of AI and SLACS represents a significant advancement in pathological research and biomarker discovery. This methodology not only enhances the accuracy of disease marker identification but also opens new avenues for research and clinical application. Citation Format: Haewook Jang, Amos Chungwon Lee, Sumin Lee, Sunghoon Kwon. Spatially Resolved Laser-Activated Cell Sorting (SLACS) and AI integration for enhanced pathological analysis and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3533.

Evaluating ChatGPT's Capabilities on Orthopedic Training Examinations: An Analysis of New Image Processing Features.

Introduction The efficacy of integrating artificial intelligence (AI) models like ChatGPT into the medical field, specifically orthopedic surgery, has yet to be fully determined. The most recent adaptation of ChatGPT that has yet to be explored is its image analysis capabilities. This study assesses ChatGPT's performance in answering Orthopedic In-Training Examination (OITE) questions, including those that require image analysis. Methods Questions from the 2014, 2015, 2021, and 2022 AAOS OITE were screened for inclusion. All questions without images were entered into ChatGPT 3.5 and 4.0 twice. Questions that necessitated the use of images were only entered into ChatGPT 4.0 twice, as this is the only version of the system that can analyze images. The responses were recorded and compared to AAOS's correct answers, evaluating the AI's accuracy and precision. Results A total of 940 questions were included in the final analysis (457 questions with images and 483 questions without images). ChatGPT 4.0 performed significantly better on questions that did not require image analysis (67.81% vs 47.59%, p<0.001). Discussion While the use of AI in orthopedics is an intriguing possibility, this evaluation demonstrates how, even with the addition of image processing capabilities, ChatGPT still falls short in terms of its accuracy. As AI technology evolves, ongoing research is vital to harness AI's potential effectively, ensuring it complements rather than attempts to replace the nuanced skills of orthopedic surgeons.

Prosthetic Visual Acuity with the PRIMA Subretinal Microchip in Patients with Atrophic Age-Related Macular Degeneration at 4 Years Follow-up

ObjectiveTo assess the efficacy and safety of the PRIMA neurostimulation system with a subretinal microchip for improving visual acuity (VA) in patients with geographic atrophy (GA) due to age-related macular degeneration (AMD) at 48-months post-implantation. DesignFirst-in-human clinical trial of the PRIMA subretinal prosthesis in patients with atrophic AMD, measuring best-corrected ETDRS VA (Clinicaltrials.gov NCT03333954). SubjectsFive patients with GA, no foveal light perception and VA of logMAR 1.3 to 1.7 (20/400-20/1000) in their worse-seeing “study” eye. MethodsIn patients subretinally implanted with a photovoltaic neurostimulation array containing 378 pixels of 100 μm in size, the VA was measured with and without the PRIMA system using ETDRS charts at 1 meter. The system’s external components, augmented reality glasses and pocket computer, provide image processing capabilities, including zoom. Main Outcome MeasuresVA using ETDRS charts with and without the system, as well as light sensitivity in the central visual field, measured by Octopus perimetry. Anatomical outcomes demonstrated by fundus photography and optical coherence tomography up to 48-months post-implantation. ResultsAll five subjects met the primary endpoint of light perception elicited by the implant in the scotoma area. In one patient the implant was incorrectly inserted into the choroid. One subject died 18-months post-implantation due to study-unrelated reason. ETDRS VA results for the remaining three subjects are reported herein. Without zoom, VA closely matched the pixel size of the implant: 1.17 ± 0.13 pixels, corresponding to mean logMAR 1.39, or Snellen 20/500, ranging from 20/438 to 20/565. Using zoom at 48 months, subjects improved their VA by 32 ETDRS letters versus baseline (SE 5.1) 95% CI[13.4,49.9], p<0.0001. Natural peripheral visual function in the treated eye did not decline after surgery, nor during the 48 months follow-up period (p=0.08). ConclusionsSubretinal implantation of PRIMA in subjects with GA suffering from profound vision loss due to AMD is feasible and well tolerated, with no reduction of natural peripheral vision up to 48-months. Prosthetic central vision provided by photovoltaic neurostimulation enabled patients to reliably recognize letters and sequences of letters, and with zoom it improved VA of up to eight ETDRS lines.

Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure

Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lower-cost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.

Effect of Freeze–Thaw Cycles on Microstructure and Hydraulic Characteristics of Claystone: A Case Study of Slope Stability from Open-Pit Mines in Wet Regions

The action of freeze–thaw (F–T) cycles of claystone exerts a profound impact on the slope stability of open-pit mines in water-rich regions. Microstructural changes are observed as a crucial factor in determining the hydraulic characteristics and mechanical behaviors of claystone. The present work integrates a micro-X-ray computed tomography (Micro-CT) scanner, equipped with image processing and three-dimensional (3D) reconstruction capabilities, employed to observe the microstructure of claystone under varying numbers of F–T cycles (0, 10, 20, 30, and 50). Furthermore, seepage numerical simulations based on Micro-CT measurements are conducted to evaluate the hydraulic characteristics. Through meticulous microscopic observation and mechanical analysis, the damage mechanism induced by F–T cycles is revealed and the evolutionary characteristics are analyzed. The two-dimensional (2D) images of 3D reconstructed models unveil the gradual initiation propagation and coalescence of an intricate fissuring network in claystone during the F–T cycles. As the number of F–T cycles increases from 0 to 50, the 3D porosity exhibits exponential growth. Additionally, the influence of F–T cycles substantially enhances the connectivity of fissures. The seepage numerical simulations demonstrate that the evolutionary progression of fissures substantially augments the number of flow paths and enhances permeability. The increase in permeability follows an exponential trend, reflecting the distribution and evolution of fissures under F–T cycles. The impact on permeability arises from a combination of micromechanical properties and the microstructure of claystones. The present research tries to elucidate the microscopic evolution of fissures and their corresponding hydraulic properties in water-saturated claystone, offering significant insights for investigating the slope stability of open-pit mines in regions.

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision.

Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event-triggered spiking, has higher efficiency and lower energy consumption. Here, this work proposes an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self-powered and event-triggered, outputting light-modulated spiking signals directly, thereby fulfilling the imperative for always-on with low-power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere five epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always-on low-power artificial vision system.