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Tehrani, Mehrdad Panahpour, Michael Droese, Toshiaki Fujii i Masayuki Tanimoto. "Distributed Source Coding Architectures for Multi-view Images". Journal of the Institute of Image Information and Television Engineers 58, nr 10 (2004): 1461–64. http://dx.doi.org/10.3169/itej.58.1461.
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Wang, Liang Hao, Ming Xi, Dong Xiao Li i Ming Zhang. "A Network-Friendly Architecture for Multi-View Video Coding (MVC)". Advanced Materials Research 121-122 (czerwiec 2010): 678–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.678.
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Multi-view Video Coding (MVC) is very promising in applicable field for its 3D effect and interactive functions (multi viewpoint). In this paper, a network-friendly architecture for MVC is proposed. To exploit temporal as well as inter-view dependencies between adjacent cameras, two main features of the coder are used: hierarchical B picture and FGS (fine granularity scalable). Coding results are shown for the proposed multi-view coder and compared to the traditional coding architectures to show that our presented coding scheme outperforms the other approaches for the tested sequence.
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Koutris, Aristotelis, Theodoros Siozos, Yannis Kopsinis, Aggelos Pikrakis, Timon Merk, Matthias Mahlig, Stylianos Papaharalabos i Peter Karlsson. "Deep Learning-Based Indoor Localization Using Multi-View BLE Signal". Sensors 22, nr 7 (2.04.2022): 2759. http://dx.doi.org/10.3390/s22072759.
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In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.
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Goktug Gurler, C., Anil Aksay, Gozde Bozdagi Akar i A. Murat Tekalp. "Architectures for multi-threaded MVC-compliant multi-view video decoding and benchmark tests". Signal Processing: Image Communication 25, nr 5 (czerwiec 2010): 325–34. http://dx.doi.org/10.1016/j.image.2010.01.002.
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Ahn, Jun Hyong, Heung Cheol Kim, Jong Kook Rhim, Jeong Jin Park, Dick Sigmund, Min Chan Park, Jae Hoon Jeong i Jin Pyeong Jeon. "Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms". Journal of Personalized Medicine 11, nr 4 (24.03.2021): 239. http://dx.doi.org/10.3390/jpm11040239.
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Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CNN architectures (AlexNet and VGG16), with similar type but different layers (ResNet101 and ResNet152), and single image-based CNN (single-view ResNet50). The sensitivity, specificity, and overall accuracy of risk prediction were estimated and compared according to CNN architecture. The study included 364 UIAs in training and 93 in test datasets. A multi-view CNN-ResNet50 exhibited a sensitivity of 81.82 (66.76–91.29)%, a specificity of 81.63 (67.50–90.76)%, and an overall accuracy of 81.72 (66.98–90.92)% for risk prediction. AlexNet, VGG16, ResNet101, ResNet152, and single-view CNN-ResNet50 showed similar specificity. However, the sensitivity and overall accuracy were decreased (AlexNet, 63.64% and 76.34%; VGG16, 68.18% and 74.19%; ResNet101, 68.18% and 73.12%; ResNet152, 54.55% and 72.04%; and single-view CNN-ResNet50, 50.00% and 64.52%) compared with multi-view CNN-ResNet50. Regarding F1 score, it was the highest in multi-view CNN-ResNet50 (80.90 (67.29–91.81)%). Our study suggests that multi-view CNN-ResNet50 may be feasible to assess the rupture risk in small-sized UIAs.
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Zhao, Haimei, Qiming Zhang, Shanshan Zhao, Zhe Chen, Jing Zhang i Dacheng Tao. "SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 7 (24.03.2024): 7460–68. http://dx.doi.org/10.1609/aaai.v38i7.28577.
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Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance. Although distilling precise 3D geometry knowledge from LiDAR data could help tackle this challenge, the benefits of LiDAR information could be greatly hindered by the significant modality gap between different sensory modalities. To address this issue, we propose a Simulated multi-modal Distillation (SimDistill) method by carefully crafting the model architecture and distillation strategy. Specifically, we devise multi-modal architectures for both teacher and student models, including a LiDAR-camera fusion-based teacher and a simulated fusion-based student. Owing to the ``identical'' architecture design, the student can mimic the teacher to generate multi-modal features with merely multi-view images as input, where a geometry compensation module is introduced to bridge the modality gap. Furthermore, we propose a comprehensive multi-modal distillation scheme that supports intra-modal, cross-modal, and multi-modal fusion distillation simultaneously in the Bird's-eye-view space. Incorporating them together, our SimDistill can learn better feature representations for 3D object detection while maintaining a cost-effective camera-only deployment. Extensive experiments validate the effectiveness and superiority of SimDistill over state-of-the-art methods, achieving an improvement of 4.8% mAP and 4.1% NDS over the baseline detector. The source code will be released at https://github.com/ViTAE-Transformer/SimDistill.
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Suwarningsih, Wiwin, Ana Heryana, Dianadewi Riswantini, Ekasari Nugraheni i Dikdik Krisnandi. "The multi-tenancy queueing system “QuAntri” for public service mall". Bulletin of Electrical Engineering and Informatics 11, nr 5 (1.10.2022): 2663–71. http://dx.doi.org/10.11591/eei.v11i5.4348.
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In the new-normal era, public services must make various adjustments to keep the community safe during the COVID-19 pandemic. The Public Service Mall is an initiative to put several public services offices in a centralized location. However, it will create a crowd of people who want access to public service. This paper evaluates multi-tenant models with the rapid adaptation of cloud computing technology for all organizations' shapes and sizes, focusing on multi-tenants and multi-services, where each tenant might have multiple services to offer. We also proposed a multi-tenant architecture that can serve queues in several places to prevent the spread of COVID-19 due to the crowd of people in public places. The design of multi-tenants and multi-services applications should consider various aspects such as security, database, data communication, and user interface. We designed and built the "QuAntri'' business logic to simplify the process for multi-services in each tenant. The developed system is expected to improve tenants' performance and reduce the crowd in the public service. We compared our agile method for system development with some of the previous multi-tenant architectures. Our experiments showed that our method overall is better than the referenced model-view-controller (MVC), model-view-presenter (MVP), and model-model-view-presenter (M-MVP).
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Debats, Oscar A., Geert J. S. Litjens i Henkjan J. Huisman. "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks". PeerJ 7 (22.11.2019): e8052. http://dx.doi.org/10.7717/peerj.8052.
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Purpose To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer. Methods A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D CNN. The three deep learning CNN architectures were trained and configured on retrospective data of 240 prostate cancer patients that received MRL images as the standard of care between January 2008 and April 2010. The MRL used ferumoxtran-10 as a contrast agent and comprised at least two imaging sequences: a 3D T1-weighted and a 3D T2*-weighted sequence. A total of 5089 lymph nodes were annotated by two expert readers, reading in consensus. A first experiment compared the performance with and without CNNs and a second experiment compared the individual contribution of the 1-view, 3-view, or 9-view architecture to the performance. The performances were visually compared using free-receiver operating characteristic (FROC) analysis and statistically compared using partial area under the FROC curve analysis. Training and analysis were performed using bootstrapped FROC and 5-fold cross-validation. Results Adding multi-view CNNs significantly (p < 0.01) reduced false positive detections. The 3-view and 9-view CNN outperformed (p < 0.01) the 1-view CNN, reducing FP from 20.6 to 7.8/image at 80% sensitivity. Conclusion Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. At the achieved level of performance, CAD for MRL may help speed up finding lymph nodes and assessing them for potential metastatic involvement.
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Fuentes Reyes, M., P. d’Angelo i F. Fraundorfer. "AN EVALUATION OF STEREO AND MULTIVIEW ALGORITHMS FOR 3D RECONSTRUCTION WITH SYNTHETIC DATA". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (13.12.2023): 1021–28. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1021-2023.
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Abstract. The reconstruction of 3D scenes from images has usually been addressed with two different strategies, namely stereo and multiview. The former requires rectified images and generates a disparity map, while the latter relies on the camera parameters and directly retrieves a depth map. For both cases, deep learning architectures have shown an outstanding performance. However, due to the differences between input and output data, the two strategies are difficult to compare on a common scene. Moreover, for remote sensing applications multi-view data is hard to acquire and the ground truth is either sparse or affected by outliers. Hence, in this article we evaluate the performance of stereo and multi-view architectures trained on synthetic data resembling remote sensing images. The data has been and processed and organized to be compatible with both kind of neural networks. For a fair comparison, training and testing are done only with two views. We focus on the accuracy of the reconstruction, as well as the impact of the depth range and the baseline of the stereo array. Results are presented for deep learning architectures and non-learning algorithms.
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Zhang, Yueping, Ao-Jan Su i Guofei Jiang. "Understanding data center network architectures in virtualized environments: A view from multi-tier applications". Computer Networks 55, nr 9 (czerwiec 2011): 2196–208. http://dx.doi.org/10.1016/j.comnet.2011.03.001.
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Mao, Wenju, Zhijie Liu, Heng Liu, Fuzeng Yang i Meirong Wang. "Research Progress on Synergistic Technologies of Agricultural Multi-Robots". Applied Sciences 11, nr 4 (5.02.2021): 1448. http://dx.doi.org/10.3390/app11041448.
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Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.
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Zhu, Binglin, Fusang Liu, Ziwen Xie, Yan Guo, Baoguo Li i Yuntao Ma. "Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season". Annals of Botany 126, nr 4 (17.03.2020): 701–12. http://dx.doi.org/10.1093/aob/mcaa046.
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Abstract Background and Aims Light interception is closely related to canopy architecture. Few studies based on multi-view photography have been conducted in a field environment, particularly studies that link 3-D plant architecture with a radiation model to quantify the dynamic canopy light interception. In this study, we combined realistic 3-D plant architecture with a radiation model to quantify and evaluate the effect of differences in planting patterns and row orientations on canopy light interception. Methods The 3-D architectures of maize and soybean plants were reconstructed for sole crops and intercrops based on multi-view images obtained at five growth dates in the field. We evaluated the accuracy of the calculated leaf length, maximum leaf width, plant height and leaf area according to the measured data. The light distribution within the 3-D plant canopy was calculated with a 3-D radiation model. Finally, we evaluated canopy light interception in different row orientations. Key Results There was good agreement between the measured and calculated phenotypic traits, with an R2 >0.97. The light distribution was more uniform for intercropped maize and more concentrated for sole maize. At the maize silking stage, 85 % of radiation was intercepted by approx. 55 % of the upper canopy region for maize and by approx. 33 % of the upper canopy region for soybean. There was no significant difference in daily light interception between the different row orientations for the entire intercropping and sole systems. However, for intercropped maize, near east–west orientations showed approx. 19 % higher daily light interception than near south–north orientations. For intercropped soybean, daily light interception showed the opposite trend. It was approx. 49 % higher for near south–north orientations than for near east–west orientations. Conclusions The accurate reconstruction of 3-D plants grown in the field based on multi-view images provides the possibility for high-throughput 3-D phenotyping in the field and allows a better understanding of the relationship between canopy architecture and the light environment.
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Efimov, A. O., I. I. Livshits, M. O. Meshcheryakov, E. A. Rogozin i V. R. Romanova. "On certain aspects of standardization and operating conditions of automated systems". Herald of Dagestan State Technical University. Technical Sciences 50, nr 4 (22.01.2024): 101–8. http://dx.doi.org/10.21822/2073-6185-2023-50-4-101-108.
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Objective. In this paper, the main aspects of the operating conditions of the AS are considered, as well as the issues of standardization of the stages of the life cycle of the AS (creation, commissioning, maintenance, etc.) at the state level. In this subject area, the technological features of building an AS based on various technical architectures are briefly considered, since both foreign processors based on x86-64 architectures and processors of domestic development based on the Advanced RISC Machine architecture are currently applicable. The use of various components of the AS requires additional study in terms of ordering the composition and configuration of specific SPI. Since each processor has a multi-level architecture, this fact objectively complicates the possibilities for full security testing and detection of all vulnerabilities. Method. In the course of the work, the threats and vulnerabilities of individual components of the AS from the point of view of intentional and unintentional threats are considered. The information on the main state standards applied to ensure the protection of information in the AS at the present time is summarized. Result. The main features of the operating conditions of the AS are considered and it is determined that the vulnerabilities of the components are due to the imperfection of the procedures for developing and covering testing of hardware and software. It is determined that in order to protect information in the AS, it is necessary to build a multi-level protection system with state accreditation. Conclusion. Proposals are presented for the application of state standardization for the protection of information in the AS, taking into account the current and prospective threat landscape, including taking into account the design features (undeclared capabilities) of the components. Overcoming threats is possible with the creation of a multi-level information protection system with state accreditation.
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Aung, Aye Nyein, Che-Wei Liao i Jeih-Weih Hung. "Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network". Future Internet 16, nr 5 (28.04.2024): 151. http://dx.doi.org/10.3390/fi16050151.
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Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separation algorithms are often built on a deep neural network (DNN) structure, which seeks to learn the complex relationship between the speech mixture and any specific speech source of interest. DNN-based speech separation algorithms outperform conventional statistics-based methods, although they typically need a lot of processing and/or a larger model size. This study presents a new end-to-end speech separation network called ESC-MASD-Net (effective speaker separation through convolutional multi-view attention and SuDoRM-RF network), which has relatively fewer model parameters compared with the state-of-the-art speech separation architectures. The network is partly inspired by the SuDoRM-RF++ network, which uses multiple time-resolution features with downsampling and resampling for effective speech separation. ESC-MASD-Net incorporates the multi-view attention and residual conformer modules into SuDoRM-RF++. Additionally, the U-Convolutional block in ESC-MASD-Net is refined with a conformer layer. Experiments conducted on the WHAM! dataset show that ESC-MASD-Net outperforms SuDoRM-RF++ significantly in the SI-SDRi metric. Furthermore, the use of the conformer layer has also improved the performance of ESC-MASD-Net.
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Xu, Kele, Kang You, Ming Feng i Boqing Zhu. "Trust-worth multi-representation learning for audio classification with uncertainty estimation". Journal of the Acoustical Society of America 153, nr 3_supplement (1.03.2023): A125. http://dx.doi.org/10.1121/10.0018383.
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Multi-view learning has been explored for audio classification tasks, exploiting different representations of audio signals, ranging from MFCC, CQT, to raw signals. The quality of each view may vary for different audio signals, and the appropriate uncertainty quantification for each view has not been fully explored. In this work, we explore a trusted multi-view learning framework for classification tasks in order to fully incorporate different views. Our framework consists of three parallel branches of Transformer architectures (Gammatone spectrogram, log-Mel and CQT) and they are combined using the uncertainty estimation of different branch. In addition to computing the classification probabilities, the uncertainty of each representation can also be obtained using the framework. We firstly calculate the evidence based on feature vectors to obtain the probabilities and the uncertainty of classification problems for Gammatone, log-Mel and CQT branch. By integrating the confidence from each of the different representations using the Dempster–Shafer theory, the classification framework can provide higher accuracy and confidence. To demonstrate the effectiveness of the proposed framework, we conduct the experiments on the GTZAN dataset. The obtained results show that our method can reach the accuracy of 83.0%, which significantly outperforms single representation-based methods while providing uncertainty estimation for different views.
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Wang, Jinglu, Bo Sun i Yan Lu. "MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 8949–56. http://dx.doi.org/10.1609/aaai.v33i01.33018949.
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In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.
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Yalcin, Ilyas, Recep Can, Candan Gokceoglu i Sultan Kocaman. "A Novel Rock Mass Discontinuity Detection Approach with CNNs and Multi-View Image Augmentation". ISPRS International Journal of Geo-Information 13, nr 6 (31.05.2024): 185. http://dx.doi.org/10.3390/ijgi13060185.
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Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara, Türkiye. A total of 34 multi-view images were collected with a remotely piloted aircraft system (RPAS), and discontinuity lines were manually delineated on a point cloud generated from these images. The lines were back-projected onto the raw images to increase the amount of data, a process we call multi-view (3D) augmentation. We further evaluated radiometric and geometric augmentation methods, the contribution of multi-view augmentation to the proposed model, and the transfer learning performance of six different CNN architectures. The highest performance was achieved with U-Net + SE-ResNeXt-50 with an F1-score of 90.6%. The CNN model trained from scratch with local features also yielded a similar F1-score (91.7%), which is the highest performance reported in the literature.
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Mrozek, Mirosław. "Multi-Agent Control System for the Movement of Uniaxial Objects". Solid State Phenomena 237 (sierpień 2015): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.237.183.
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Multi-agent systems are used mainly in IT solutions and control groups of robots. From the point of view of classical control architectures, they are a kind of distributed systems in which nodes perform advanced algorithms, usually associated with the technology of artificial intelligence, and they can be considered as agents. The article describes the multi-agents control system of objects of uniaxial movements. An example of such a system to control a repository with movable racks with electric motors is presented. Each rack acts as an agent through the implemented control of the resources of embedded microcontrollers. Such a system provides high quality control, guaranteeing long-lasting, trouble-free operation while maintaining the safety of both service and stored items.
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Griffiths, David, i Jan Boehm. "A Review on Deep Learning Techniques for 3D Sensed Data Classification". Remote Sensing 11, nr 12 (25.06.2019): 1499. http://dx.doi.org/10.3390/rs11121499.
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Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.
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PARIS, NICOLAS. "POMPC: A C LANGUAGE FOR DATA PARALLELISM". International Journal of Modern Physics C 04, nr 01 (luty 1993): 85–96. http://dx.doi.org/10.1142/s0129183193000094.
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POMPC is a parallel language dedicated to the programming of Massively Parallel Computers according to a synchronous Data Parallel model. It is an extension of the ANSI C language and follows its philosophy. Parallelism is explicitly handled by the definition of collections of parallel variables and the definition of communication primitives. A methodology is presented in order to easily port the language on different target architectures. Virtualization is introduced to handle simultaneously several collections of different sizes and shapes. Virtualization management is a key point of the compilation process. Programmer, architecture, compilation and system points of view lead to a special implementation of the virtualization mixing physical and virtual parallel objects. The implementation of the virtualization is adapted for the development of communication libraries and also adapted to enlarge the asynchronous sections of code for SPMD architecture. The portability of the POMPC language is validated by several implementations for mono/multi-process simulation on UNIX machines, for the Connection Machine CM-2, for the MasPar MP-1 and a compiler is in preparation for the iPSC-860.
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Alouane-Ksouri, Sonia, i Minyar Sassi Hidri. "Fuzzy Learning of Co-Similarities from Large-Scale Documents". International Journal of Fuzzy System Applications 4, nr 4 (październik 2015): 70–86. http://dx.doi.org/10.4018/ijfsa.2015100104.
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To analyze and explore large textual corpus, we are generally limited by the available main memory. This may lead to a proliferation of processor load due to greedy computing. The authors propose to deal with this problem to compute co-similarities from large-scale documents. The authors propose to enhance co-similarity learning by upstream and downstream parallel computing. The first deploys the fuzzy linear model in a Grid environment. The second deals with multi-view datasets while introducing different architectures by using several instances of a fuzzy triadic similarity algorithm.
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Yoshida, Naoto. "Homeostatic Agent for General Environment". Journal of Artificial General Intelligence 8, nr 1 (7.03.2018): 1–22. http://dx.doi.org/10.1515/jagi-2017-0001.
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AbstractOne of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.
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Agarla, Mirko, Paolo Napoletano i Raimondo Schettini. "Quasi Real-Time Apple Defect Segmentation Using Deep Learning". Sensors 23, nr 18 (14.09.2023): 7893. http://dx.doi.org/10.3390/s23187893.
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Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
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Sharifi, Ali Asghar, Ali Zoljodi i Masoud Daneshtalab. "TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction". Sensors 24, nr 17 (1.09.2024): 5696. http://dx.doi.org/10.3390/s24175696.
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Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.
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Thompson, Alison, Kelly Thorp, Matthew Conley, Diaa Elshikha, Andrew French, Pedro Andrade-Sanchez i Duke Pauli. "Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton". Remote Sensing 11, nr 6 (23.03.2019): 700. http://dx.doi.org/10.3390/rs11060700.
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Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.
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Zou, Yanmei, Hongshan Yu, Zhengeng Yang, Zechuan Li i Naveed Akhtar. "Improved MLP Point Cloud Processing with High-Dimensional Positional Encoding". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 7 (24.03.2024): 7891–99. http://dx.doi.org/10.1609/aaai.v38i7.28625.
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Multi-Layer Perceptron (MLP) models are the bedrock of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength. We first develop an “abstraction and refinement” (ABS-REF) view for the neural modeling of point clouds. This view elucidates that whereas the early models focused on the ABS stage, the more recent techniques devise sophisticated REF stages to attain performance advantage in point cloud processing. We then borrow the concept of “positional encoding” from transformer literature, and propose a High-dimensional Positional Encoding (HPE) module, which can be readily deployed to MLP based architectures. We leverage our module to develop a suite of HPENet, which are MLP networks that follow ABS-REF paradigm, albeit with a sophisticated HPE based REF stage. The developed technique is extensively evaluated for 3D object classification, object part segmentation, semantic segmentation and object detection. We establish new state-of-the-art results of 87.6 mAcc on ScanObjectNN for object classification, and 85.5 class mIoU on ShapeNetPart for object part segmentation, and 72.7 and 78.7 mIoU on Area-5 and 6-fold experiments with S3DIS for semantic segmentation. The source code for this work is available at https://github.com/zouyanmei/HPENet.
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Nathanael, Oliverio Theophilus, i Simeon Yuda Prasetyo. "Color and Attention for U : Modified Multi Attention U-Net for a Better Image Colorization". JOIV : International Journal on Informatics Visualization 8, nr 3 (30.09.2024): 1453. http://dx.doi.org/10.62527/joiv.8.3.1828.
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Image colorization is a tedious task that requires creativity and understanding of the image context and semantic information. Many models have been made by harnessing various deep learning architectures to learn the plausible colorization. With the rapid discovery of new architecture and image generation techniques, more powerful options can be explored and improved for image colorization tasks. This research explores a new architecture to colorize an image by using pre-trained embeddings on U-Net combined with several attention modules across the model. Using embeddings from a pre-trained classifier provides a high-level feature extraction from the image. Conversely, multi-attention gives a little taste of image segmentation so that the model can distinguish objects in the image and further enhance the additional information given by the pre-trained embeddings. Adversarial training is also utilized as a normalization to make the generated image more realistic. This research preferred Parch GAN over base GAN as the discriminator model to ensure that the colorization has a consistent quality across all patches. The study shows that this U-Net modification can improve the generated image quality compared to a normal U-Net. The proposed architecture reaches an FID of 48.6253, SSIM of 0.8568, and PSNR of 19.7831 by only training it for 25 epochs; hence, this research offers another view of image colorization by using modules that are often used for image segmentation tasks.
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Chen, Kun, Xin Li i Huaiqing Wang. "On the model design of integrated intelligent big data analytics systems". Industrial Management & Data Systems 115, nr 9 (19.10.2015): 1666–82. http://dx.doi.org/10.1108/imds-03-2015-0086.
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Purpose – Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model. Design/methodology/approach – In the conceptual CI framework as proposed by Schut (2010), a CI design should be comprised of a general model, which has formal form for verification and validation, and also a specific model, which is an implementable system architecture. After analyzing the requirements of system integration in big data environments, the authors apply the CI framework to resolve the integration problem. In the model instantiation, the authors use multi-agent paradigm as the specific model, and the hierarchical colored Petri Net (PN) as the general model. Findings – First, multi-agent paradigm is a good implementation for reuse and integration of big data analytics modules in an agile and loosely coupled method. Second, the PN models provide effective simulation results in the system design period. It gives advice on business process design and workload balance control. Third, the CI framework provides an incrementally build and deployed method for system integration. It is especially suitable to the dynamic data analytics environment. These findings have both theoretical and managerial implications. Originality/value – In this paper, the authors propose a CI framework, which includes both practical architectures and theoretical foundations, to solve the system integration problem in big data environment. It provides a new point of view to dynamically integrate large-scale modules in an organization. This paper also has practical suggestions for Chief Technical Officers, who want to employ big data technologies in their companies.
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Guptha, Sharada, i Murundi N. Eshwarappa. "Breast cancer detection through attention based feature integration model". IAES International Journal of Artificial Intelligence (IJ-AI) 13, nr 2 (1.06.2024): 2254. http://dx.doi.org/10.11591/ijai.v13.i2.pp2254-2264.
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<span lang="EN-US">Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM) module integrates the left and right activation maps for a similar projection is used to create a spatial attention map that highlights the impact of asymmetries. Here the module's focus is on data gathering through medio-lateral oblique (MLO) and bilateral craniocaudal (CC) for each breast to develop an attention module. The proposed AFIM model generates using spatial attention maps obtained from the identical image through other breasts to identify bilaterally uneven areas and</span><span lang="EN-US">class activation map (CAM) generated from two similar breast images to emphasize the feature channels connected to a single lesion in a breast. AFIM model may easily be included in ResNet-style architectures to develop multi-view classification models.</span>
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Muhamad, Wardani, i Wawa Wikusna. "Software Architecture of E-assessment on Higher Education". IJAIT (International Journal of Applied Information Technology) 1, nr 02 (7.12.2017): 102. http://dx.doi.org/10.25124/ijait.v1i02.1030.
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Computer technology has been used to support the learning process at the university. Learning process, generally involved students and teachers in order to learn about materials on subject courses and also evaluate student competencies regularly. Teachers can evaluate student competencies or knowledge by e-assessment. E-assessment is one of the domains of e-learning which involves the use computer in assessment, includes: setting, delivery, marking and reporting of assessments. The Major benefit of the e - assessment system is its flexibility in term of global access and devices used to access. When developing an e-assessment system, we have two focuses on multi-dimensional approach, such as user friendly and student centric nature. Because of its complexity, software architecture need to define so software developer will develop software properly. By designing software architecture, view of the system that includes the system components, the behavior of those components, and the ways the components interact could clearly define. Architecture Description Language (ADL) has been used to describe software, because it provides a concrete syntax and formal framework for characterizing architectures. As the result, the design of e-assessment system architecture can meet the needs of attribute quality. The use of notation to explain ADL is able to provide a complete description than simply explaining ADL is using text. Furthermore, the e-assessment system architecture design is expected to be used as a reference for software development in establishing an e-assessment system.
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Chen, Hanyue, Wenjiang Huang, Wang Li, Zheng Niu, Liming Zhang i Shihe Xing. "Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture". Remote Sensing 10, nr 10 (13.10.2018): 1630. http://dx.doi.org/10.3390/rs10101630.
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View angle effects present in crop canopy spectra are critical for the retrieval of the crop canopy leaf area index (LAI). In the past, the angular effects on spectral vegetation indices (VIs) for estimating LAI, especially in crops with different plant architectures, have not been carefully assessed. In this study, we assessed the effects of the view zenith angle (VZA) on relationships between the spectral VIs and LAI. We measured the multi-angular hyperspectral reflectance and LAI of two cultivars of winter wheat, erectophile (W411) and planophile (W9507), across different growing seasons. The reflectance of each angle was used to calculate a variety of VIs that have already been published in the literature as well as all possible band combinations of Normalized Difference Spectral Indices (NDSIs). The above indices, along with the raw reflectance of representative bands, were evaluated with measured LAI across the view zenith angle for each cultivar of winter wheat. Data analysis was also supported by the use of the PROSAIL (PROSPECT + SAIL) model to simulate a range of bidirectional reflectance. The study confirmed that the strength of linear relationships between different spectral VIs and LAI did express different angular responses depending on plant type. LAI–VI correlations were generally stronger in erectophile than in planophile wheat types, especially at the zenith angle where the background is expected to be more evident for erectophile type wheat. The band combinations and formulas of the indices also played a role in shaping the angular signatures of the LAI–VI correlations. Overall, off-nadir angles served better than nadir angle and narrow-band indices, especially NDSIs with combinations of a red-edge (700~720 nm) and a green band, were more useful for LAI estimation than broad-band indices for both types of winter wheat. But the optimal angles much differed between two plant types and among various VIs. High significance (R2 > 0.9) could be obtained by selecting appropriate VIs and view angles on both the backward and forward scattering direction. These results from the in-situ measurements were also corroborated by the simulation analysis using the PROSAIL model. For the measured datasets, the highest coefficient was obtained by NDSI(536,720) at −35° in the backward (R2 = 0.971) and NDSI(571,707) at 55° in the forward scattering direction (R2 = 0.984) for the planophile and erectophile varieties, respectively. This work highlights the influence of view geometry and plant architecture. The identification of crop plant type is highly recommended before using remote sensing VIs for the large-scale mapping of vegetation biophysical variables.
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Dongarra, Jack, Laura Grigori i Nicholas J. Higham. "Numerical algorithms for high-performance computational science". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, nr 2166 (20.01.2020): 20190066. http://dx.doi.org/10.1098/rsta.2019.0066.
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A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
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Cipolla, Vittorio, Andri Dine, Andrea Viti i Vincenzo Binante. "MDAO and Aeroelastic Analyses of Small Solar-Powered UAVs with Box-Wing and Tandem-Wing Architectures". Aerospace 10, nr 2 (20.01.2023): 105. http://dx.doi.org/10.3390/aerospace10020105.
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The market of solar-powered Unmanned Aerial Vehicles (UAVs) for defence purposes and drone services is expected to grow by a factor of more than 2 in the next decade. From an aircraft design perspective, the main challenge is the scalability of the proposed architectures, which is needed to increase the payload capabilities. Beside some successful examples of wing-tail UAVs, some newcomers are developing prototypes with tandem-wing architectures, hence enlarging the possible design. The present paper aims to introduce a further step in this direction, taking also the box-wing architecture into account to show how the presence of wing tip joiners can provide benefits from the aeroelastic point of view. UAVs with take-off mass within 25 kg are considered and the main tools adopted are presented. These are an in-house developed Multi-Disciplinary Analysis and Optimization (MDAO) code called SD2020 and the open source aeroelastic code ASWING, both presented together with an assessment of their accuracy by means of higher fidelity numerical results. SD2020 results are presented for the case of small box-wing solar UAVs optimized to achieve the longest endurance, focusing on the strategy implemented to achieve feasible solutions under an assigned set of constraints. Further results are presented for comparable box-wing and tandem-wing UAVs from both the aerodynamic and aeroelastic standpoints. Whereas the aerodynamic advantages introduced by the box-wing are marginal, significant advantages result from the aeroelastic analyses which indicate that, if the joiners are removed from the box-wing configuration, safety margin from flutter speed is halved and the bending-torsion divergence occurs at relatively low speed values.
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Ford, Kenneth M., James Allen, Niranjan Suri, Patrick J. Hayes i Robert Morris. "PIM: A Novel Architecture for Coordinating Behavior of Distributed Systems". AI Magazine 31, nr 2 (28.07.2010): 9. http://dx.doi.org/10.1609/aimag.v31i2.2261.
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Process integrated mechanisms (PIM) offer a new approach to the problem of coordinating the activity of physically distributed systems or devices. Current approaches to coordination all have well-recognized strengths and weaknesses. We propose a novel architecture to add to the mix, called the Process Integrated Mechanism (PIM), which enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to its rejection in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM models offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. PIM models enable coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a MAS.The PIM model provides an ease of programming with advantages over both multi-agent sys-tems and centralized architectures. It has the robustness of a multi-agent system without the significant complexity and overhead required for inter-agent communication and negotiation. In contrast to centralized approaches, it does not require managing the large amounts of data that the coordinating process needs to compute a global view. In a PIM, the process moves to the data and may perform computations on the components where the data is locally available, sharing only the information needed for coordination of the other components. While there are many remaining research issues to be addressed, we believe that PIMs offer an important and novel tech-nique for the control of distributed systems.
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Hidri, Minyar Sassi, Sonia Alouane Ksouri i Kamel Barkaoui. "Grid-Based Fuzzy Processing for Parallel Learning the Document Similarities". International Journal of Service Science, Management, Engineering, and Technology 5, nr 1 (styczeń 2014): 66–83. http://dx.doi.org/10.4018/ijssmet.2014010104.
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Document co-clustering methods allow to efficiently capture high-order similarities between objects described by rows and columns of a data matrix. In Alouane et al. (2013), a method for simultaneous computation of similarity matrices between objects (documents or sentences) and between descriptors (sentences or words), each one being built on the other one, according to a fuzzy triadic model based on the three-partite graph. Because of the development of the Web and the high availability of storage spaces, documents become more accessible. This makes the fuzzy computing very expensive. In the present case, the development of fuzzification algorithms of fuzzification requires the integration of a deployment platform with the required processing power. The choice of a grid architecture seems to be an appropriate answer to our needs since it allows us to distribute the processing over all the machines of the platform, thus creating the illusion of a virtual computer able to solve important computing problems which require very long run times in a single machine environment. The authors propose to enhance similarity by upstream and downstream parallel processing. The first deploys the fuzzy linear model in a Grid environment. The second deals with multi-view datasets while introducing different architectures by using several instances of a fuzzy triadic similarity algorithm.
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Xiang, Lichuan, Lukasz Dudziak, Mohamed S. Abdelfattah, Thomas Chau, Nicholas D. Lane i Hongkai Wen. "Zero-Cost Operation Scoring in Differentiable Architecture Search". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 9 (26.06.2023): 10453–63. http://dx.doi.org/10.1609/aaai.v37i9.26243.
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We formalize and analyze a fundamental component of dif- ferentiable neural architecture search (NAS): local “opera- tion scoring” at each operation choice. We view existing operation scoring functions as inexact proxies for accuracy, and we find that they perform poorly when analyzed empir- ically on NAS benchmarks. From this perspective, we intro- duce a novel perturbation-based zero-cost operation scor- ing (Zero-Cost-PT) approach, which utilizes zero-cost prox- ies that were recently studied in multi-trial NAS but de- grade significantly on larger search spaces, typical for dif- ferentiable NAS. We conduct a thorough empirical evalu- ation on a number of NAS benchmarks and large search spaces, from NAS-Bench-201, NAS-Bench-1Shot1, NAS- Bench-Macro, to DARTS-like and MobileNet-like spaces, showing significant improvements in both search time and accuracy. On the ImageNet classification task on the DARTS search space, our approach improved accuracy compared to the best current training-free methods (TE-NAS) while be- ing over 10× faster (total searching time 25 minutes on a single GPU), and observed significantly better transferabil- ity on architectures searched on the CIFAR-10 dataset with an accuracy increase of 1.8 pp. Our code is available at: https://github.com/zerocostptnas/zerocost operation score.
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Henke, Michael, i Evgeny Gladilin. "Virtual Laser Scanning Approach to Assessing Impact of Geometric Inaccuracy on 3D Plant Traits". Remote Sensing 14, nr 19 (21.09.2022): 4727. http://dx.doi.org/10.3390/rs14194727.
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In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.
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Harweg, Thomas, Annika Peters, Daniel Bachmann i Frank Weichert. "CNN-Based Deep Architecture for Health Monitoring of Civil and Industrial Structures Using UAVs". Proceedings 42, nr 1 (14.11.2019): 69. http://dx.doi.org/10.3390/ecsa-6-06640.
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Health monitoring of civil and industrial structures has been gaining importance since the collapse of the bridge in Genoa (Italy). It is vital for the creation and maintenance of reliable infrastructure. Traditional manual inspections for this task are crucial but time consuming. We present a novel approach for combining Unmanned Aerial Vehicles (UAVs) and artificial intelligence to tackle the above-mentioned challenges. Modern architectures in Convolutional Neural Networks (CNNs) were adapted to the special characteristics of data streams gathered from UAV visual sensors. The approach allows for automated detection and localization of various damages to steel structures, coatings, and fasteners, e.g., cracks or corrosion, under uncertain and real-life environments. The proposed model is based on a multi-stage cascaded classifier to account for the variety of detail level from the optical sensor captured during an UAV flight. This allows for reconciliation of the characteristics of gathered image data and crucial aspects from a steel engineer’s point of view. To improve performance of the system and minimize manual data annotation, we use transfer learning based on the well-known COCO dataset combined with field inspection images. This approach provides a solid data basis for object localization and classification with state-of-the-art CNN architectures.
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Tamakloe, Reuben Yao, Michael Kweku Edem Donkor i Keshaw Singh. "Fabrication and Study of Power- Output of MultiChamber Microbial Fuel Cells (Mfcs) With Clay as Ion Exchange Partition". European Scientific Journal, ESJ 13, nr 30 (31.10.2017): 173. http://dx.doi.org/10.19044/esj.2017.v13n30p173.
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The main challenges in the construction of microbial fuel cells (MFCs) are the identification of materials, designs, and architectures that may maximize power generation efficiency and fabrication cost. In view of these facts, an attempt was made to design and fabricate Multi – Chamber MFCs of different configuration using locally available Mfensi clay as ionexchange partitions. The performance of each micro-cell, combined effect of the total system as one cell, and the overall performance were studied. The volume of each chamber of these cells was approximately 130 cm3 . It was found that the wastewater of chemical oxygen demand (COD) that was 6340 gm/L used in the MFCs yielded a maximum open circuit voltage (OCV) of 1421 ± 30 mV. The peak power density of 33.30 mW/cm2 (0.037 mA/cm2 ) at 1000 Ω was normalized to the anode surface area
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Wang, Yantian, Haifeng Li, Peng Jia, Guo Zhang, Taoyang Wang i Xiaoyun Hao. "Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images". Sensors 19, nr 23 (29.11.2019): 5270. http://dx.doi.org/10.3390/s19235270.
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Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method.
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Assouma, Abdoul Kamal, Tahirou Djara i Abdou-Aziz Sobabe. "Multi-Biometrics: Survey and Projection of a New Biometric System". International Journal of Engineering and Advanced Technology 12, nr 3 (28.02.2023): 80–87. http://dx.doi.org/10.35940/ijeat.c4008.0212323.
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Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin biometrics which allows the use of pure biometric modalities and metadata in a feature fusion strategy. The main objective of this paper is to present an overview of biometrics as bordered in the literature with a particular focus on multibiometrics and to propose a model of a multi-origin biometric system using pure biometric and soft biometric modalities in a feature-level fusion strategy. The curvelet transformation and the order statistics are proposed respectively for the extraction the feature of the pure biometric modalities, and for the selection of the relevant feature of each modality in order to ensure a good level of discrimination of the individuals. In this paper, we have presented the overview of biometrics through its concepts, modalities, advantages, disadvantages and implementation architectures. A focus has been put on multi-biometrics with the presentation of a harmonized process for feature fusion. For the experiments, we proposed a global model for feature fusion in a multi-origin system using face and iris modalities as pure biometrics, and facial skin color as metadata. This system and the results will be presented in future work.
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Shourov, Chowdhury Erfan, Mahasweta Sarkar, Arash Jahangiri i Christopher Paolini. "Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures". Future Transportation 1, nr 2 (12.09.2021): 387–413. http://dx.doi.org/10.3390/futuretransp1020022.
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Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, due to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections.
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Dang, Thai-Viet, i Ngoc-Tam Bui. "Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation". Electronics 12, nr 3 (20.01.2023): 533. http://dx.doi.org/10.3390/electronics12030533.
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In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.
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Fuschini, Franco, Marina Barbiroli, Giovanna Calò, Velio Tralli, Gaetano Bellanca, Marco Zoli, Jinous Shafiei Dehkordi, Jacopo Nanni, Badrul Alam i Vincenzo Petruzzelli. "Multi-Level Analysis of On-Chip Optical Wireless Links". Applied Sciences 10, nr 1 (25.12.2019): 196. http://dx.doi.org/10.3390/app10010196.
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Networks-on-chip are being regarded as a promising solution to meet the on-going requirement for higher and higher computation capacity. In view of future kilo-cores architectures, electrical wired connections are likely to become inefficient and alternative technologies are being widely investigated. Wireless communications on chip may be therefore leveraged to overcome the bottleneck of physical interconnections. This work deals with wireless networks-on-chip at optical frequencies, which can simplify the network layout and reduce the communication latency, easing the antenna on-chip integration process at the same time. On the other end, optical wireless communication on-chip can be limited by the heavy propagation losses and the possible cross-link interference. Assessment of the optical wireless network in terms of bit error probability and maximum communication range is here investigated through a multi-level approach. Manifold aspects, concurring to the final system performance, are simultaneously taken into account, like the antenna radiation properties, the data-rate of the core-to core communication, the geometrical and electromagnetic layout of the chip and the noise and interference level. Simulations results suggest that communication up to some hundreds of μm can be pursued provided that the antenna design and/or the target data-rate are carefully tailored to the actual layout of the chip.
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ATLI, İbrahim, i Osman Serdar GEDİK. "3D reconstruction of coronary arteries using deep networks from synthetic X-ray angiogram data". Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 64, nr 1 (30.06.2022): 1–20. http://dx.doi.org/10.33769/aupse.1020175.
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Cardiovascular disease (CVD) is one of the most common health problems that are responsible for one-third of all deaths around the globe. Although X-Ray angiography has deficiencies such as two-dimensional (2D) representation of three dimensional (3D) structures, vessel overlapping, noisy background, the existence of other tissues/organs in images, etc., it is used as the gold standard technique for the diagnosis and in some cases treatment of CVDs. To overcome the deficiencies, great efforts have been drawn on retrieval of actual 3D representation of coronary arterial tree from 2D X-ray angiograms. However, the proposed algorithms are based on analytical methods and enforce some constraints. With the evolution of deep neural networks, 3D reconstruction from images can be achieved effectively. In this study, we propose a new data structure for the representation of objects in a tubular shape for 3D reconstruction of arteries using deep learning. Moreover, we propose a method to generate synthetic coronaries from data of real subjects. Then, we validate tubular shape representation using 3 typical deep learning architectures with synthetic X-ray data we produced. The input to deep learning architectures is multi-view segmented X-Ray images and the output is the structured tubular representation. We compare results qualitatively in terms of visual appearance and quantitatively in terms of Chamfer Distance and Mean Squared Error. The results demonstrate that tubular representation has promising performance in 3D reconstruction of coronaries. We observe that convolutional neural network (CNN) based architectures yield better 3D reconstruction performance with 9.9e-3 on Chamfer Distance. On the other hand, LSTM-based network fails to learn the coronary tree structure and we conclude that LSTMs are not appropriate for auto-regression problems as depicted in this study.
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Iturria-Rivera, Pedro Enrique, Han Zhang, Hao Zhou, Shahram Mollahasani i Melike Erol-Kantarci. "Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)". Sensors 22, nr 14 (19.07.2022): 5375. http://dx.doi.org/10.3390/s22145375.
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Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization.
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Catruna, Andy, Pavel Betiu, Emanuel Tertes, Vladimir Ghita, Emilian Radoi, Irina Mocanu i Mihai Dascalu. "Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images". Electronics 12, nr 24 (9.12.2023): 4947. http://dx.doi.org/10.3390/electronics12244947.
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The orientation of objects plays an important role in accurate predictions for the tasks of classification, detection, and trajectory estimation. This is especially important in the automotive domain, where estimating an accurate car orientation can significantly impact the effectiveness of the other prediction tasks. This work presents Car Full View (CFV), a novel dataset for car orientation prediction from images obtained by video recording all possible angles of individual vehicles in diverse scenarios. We developed a tool to semi-automatically annotate all the video frames with the respective car angle based on the walking speed of the recorder and manually annotated key angles. The final dataset contains over 23,000 images of individual cars along with fine-grained angle annotations. We study the performance of three state-of-the-art deep learning architectures on this dataset in three different learning settings: classification, regression, and multi-objective. The top result of 3.39° in circular mean absolute error (CMAE) shows that the model accurately predicts car orientations for unseen vehicles and images. Furthermore, we test the trained models on images from two different datasets and show their generalization capability to realistic images. We release the dataset and the best models while publishing a web service to annotate new images.
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Shi, Jing. "Construction and Application of Three-dimensional Information Management System for Intelligent Buildings Integrating BIM and GIS Technologies". Scalable Computing: Practice and Experience 25, nr 4 (16.06.2024): 2985–3000. http://dx.doi.org/10.12694/scpe.v25i4.2939.
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Smart building technologies are widely used in all aspects of building structure, services, and management, helping to create a more comfortable, safe and convenient building environment. Building Information Modelling (BIM) and Geographic Information System (GIS) technologies are both widely used intelligent building technologies, and their combination can improve the analytical ability of spatial environment to a certain extent. However, it is difficult to manage them due to the huge amount of data in the Three-Dimensional (3D) information of intelligent buildings. Therefore, it is very important to improve the information management ability of intelligent building 3D information management systems (Moballeghi et al. 2023; Mahamood and Fathi 2022). BIM and GIS technologies were used to build a 3D information management system for intelligent buildings more effectively. The design and development principles of the information management system were explained, and the overall framework of the system was also designed. Research was conducted on feature extraction and matching through an improved scale invariant feature transformation algorithm to enhance the information classification and management capability of the intelligent building 3D information management system. In addition, the improvement measure for SIFI algorithm was to reduce pixel processing to reduce its memory size. The study explained the preprocessing of model normalization before feature extraction and matching. The coordinate system rotation normalization of building 3D models was achieved through principal component analysis. Finally, the calculation of covariance matrix was explained. The number of pyramid image groups was adopted to further improve the scale space and enhance computational efficiency. The Hessian matrix was introduced to eliminate unstable fixed points. And the purity of feature point matching through similarity coefficients was improved. In addition, a modified multi-view convolutional neural network was used to classify the feature data, and a modified classification architecture was designed to build a 3D model based on this algorithm to enhance its information classification management capabilities. The study explained the calculation of view weights and global descriptors and described the fully connected and classification architectures. The results showed that the improved scale-invariant feature conversion algorithm achieved a matching accuracy of 98.3% and takes only 17 s. Meanwhile, the proposed multi-view convolutional neural network achieved an accuracy of 97.6% and an F1 value of 96.4% for the classification of 3D information of intelligent buildings. Among the six types of 3D building models selected, the method achieved the highest accuracy of 94.26% and was more stable. It shows that the proposed method of 3D information management of intelligent buildings has obvious classification advantages and provides a new technical reference for the information development of intelligent buildings.
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Wang, Yuhao, Binxiu Liang, Meng Ding i Jiangyun Li. "Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery". Remote Sensing 11, nr 1 (22.12.2018): 20. http://dx.doi.org/10.3390/rs11010020.
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Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.
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Wang, Weihang, Peilin Liu, Rendong Ying, Jun Wang, Jiuchao Qian, Jialu Jia i Jiefeng Gao. "A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements". Sensors 19, nr 3 (11.02.2019): 729. http://dx.doi.org/10.3390/s19030729.
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State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.