Relevant bibliographies by topics / 360 neural networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic '360 neural networks'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "360 neural networks"

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Jitendra, Sunte. "Scientific Reason for Divorce in Couples of Marriage Life." Journal of Applied Nursing Research and Education 3, no. 2 (2025): 35–38. https://doi.org/10.5281/zenodo.15516598.

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<em>In the day-to-day present scenario in social life, divorce is going to see a lot of statistics. This is a bad remark in social life; however, one needs to resolve the issue and make it into a balanced situation. In the current scenario, there will be a lot of records of marriages that break up and lead to divorce. In this paper one can identify the major reason for the problem and rectify the same issue through the scientific way. In understanding the universe, there will be 360-degree artificial neural networks whose origin and destination are from the bottom lower sky and the top upper s
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Sychev, D. A. "Pharmacotherapy Safety 360°: NOLI NOCERE!" Pharmacogenetics and Pharmacogenomics, no. 1 (July 19, 2023): 3–5. http://dx.doi.org/10.37489/2588-0527-2023-1-3-5.

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The Russian Congress «Pharmacotherapy Safety 360°: NOLI NOCERE!» was successfully held at the Russian Ministry of Health in May 2023, providing a high-level, expert platform to discuss current and topical issues of pharmacovigilance and pharmacotherapy safety for different patient groups, including pediatrics, gerontology and geriatrics, pregnant women, patients with orphan and oncological diseases. Extensive scientific topics covered the most significant aspects of the pharmacotherapy safety in various fields, including cardiology, gastroenterology, pulmonology and allergology, endocrinology,
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Deepali, M. Bongulwar, and N. Talbar S. "Robust Convolutional Neural Network Model For Recognition of Fruits." Indian Journal of Science and Technology 14, no. 45 (2021): 3318–34. https://doi.org/10.17485/IJST/v14i45.1493.

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<strong>Objectives:</strong>&nbsp;To develop a model for the automatic recognition of fruits utilizing deep learning techniques.&nbsp;<strong>Methods:</strong>&nbsp;We have designed a fruit classification and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 different categories. The classification is challenging as the images comprise different fruits of the same color and shape, overlapped fruits, the background is not homogenous, and with different light effects
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Liu, Kevin. "Comparison of different Convolutional Neural Network models on Fruit 360 Dataset." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 85–94. http://dx.doi.org/10.54097/hset.v34i.5385.

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Numerous Convolutional Neural Networks emerged in the past decade, each varies in accuracy, speed, and architecture. From AlexNet to ResNet, CNN models have been developing rapidly, and the architecture of the models become more complicated. These models are known for their accuracy on ImageNet, so the topic of this research is to explore how CNN models can perform differently on the Fruit 360 dataset. A model constructed specifically in this research and three significant models developed in the past decade are applied to the Fruit 360 dataset for result comparison: VGG-16, ResNet-50, MobileN
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Kumar, Shashwat, Lalit Bhagat, Antony Franklin A., and Jiong Jin. "Multi-neural network based tiled 360°video caching with Mobile Edge Computing." Journal of Network and Computer Applications 201 (May 2022): 103342. http://dx.doi.org/10.1016/j.jnca.2022.103342.

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Beaucamp, B., T. Leduc, V. Tourre, and M. Servières. "THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 33–40. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-33-2022.

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Abstract. 360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained
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Alvarez-Rodríguez, Sergio, and Francisco G. Peña-Lecona. "Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder." Sensors 23, no. 20 (2023): 8347. http://dx.doi.org/10.3390/s23208347.

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Artificial neural networks are a powerful tool for managing data that are difficult to process and interpret. This article presents the design and implementation of backpropagated multilayer artificial neural networks, structured with a vector input, hidden layers, and an output node, for information processing generated by an optical encoder based on the polarization of light. A machine learning technique is proposed to train the neural networks such that the system can predict with remarkable accuracy the angular position in which the rotating element of the neuro-encoder is located based on
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Lee, Dongwon, Minji Choi, and Joohyun Lee. "Prediction of Head Movement in 360-Degree Videos Using Attention Model." Sensors 21, no. 11 (2021): 3678. http://dx.doi.org/10.3390/s21113678.

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In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models ba
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Barmpoutis, Panagiotis, Tania Stathaki, Kosmas Dimitropoulos, and Nikos Grammalidis. "Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures." Remote Sensing 12, no. 19 (2020): 3177. http://dx.doi.org/10.3390/rs12193177.

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The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire
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Zhang, Xin, Degang Yang, Tingting Song, Yichen Ye, Jie Zhou, and Yingze Song. "Classification and Object Detection of 360° Omnidirectional Images Based on Continuity-Distortion Processing and Attention Mechanism." Applied Sciences 12, no. 23 (2022): 12398. http://dx.doi.org/10.3390/app122312398.

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The use of 360° omnidirectional images has occurred widely in areas where comprehensive visual information is required due to their large visual field coverage. However, many extant convolutional neural networks based on 360° omnidirectional images have not performed well in computer vision tasks. This occurs because 360° omnidirectional images are processed into plane images by equirectangular projection, which generates discontinuities at the edges and can result in serious distortion. At present, most methods to alleviate these problems are based on multi-projection and resampling, which ca
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Dissertations / Theses on the topic "360 neural networks"

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Sendjasni, Abderrezzaq. "Objective and subjective quality assessment of 360-degree images." Electronic Thesis or Diss., Poitiers, 2023. http://www.theses.fr/2023POIT2251.

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Les images à 360 degrés, aussi appelées images omnidirectionnelles, sont au cœur des contenus immersifs. Avec l’augmentation de leur utilisation notamment grâce à l’expérience interactive et immersive qu’ils offrent, il est primordial de garantir une bonne qualité d’expérience (QoE). Cette dernière est considérablement impactée par la qualité du contenu lui-même. En l’occurrence, les images à 360 degrés, comme tout type de signal visuel, passent par une séquence de processus comprenant l’encodage, la transmission, le décodage et le rendu. Chacun de ces processus est susceptible d’introduire de
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Guimard, Quentin. "Deep learning pour le streaming adaptatif de vidéos à 360° en réalité virtuelle." Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ4120.

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La réalité virtuelle (VR) a évolué de manière significative ces dernières années. Les casques immersifs devenant de plus en plus abordables et populaires, de nombreuses applications sont à l'horizon, des vidéos à 360° aux formations interactives en passant par les environnements virtuels collaboratifs. Cependant, pour atteindre des niveaux élevés de qualité perçue, la bande passante du réseau et les ressources de calcul nécessaires peuvent être supérieures de plusieurs ordres de grandeur à celles requises pour un contenu 2D traditionnel.Pour pallier ce problème, des stratégies de streaming qui
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Kovvuri, Prem. "Investigation of Different Video Compression Schemes Using Neural Networks." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/320.

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Image/Video compression has great significance in the communication of motion pictures and still images. The need for compression has resulted in the development of various techniques including transform coding, vector quantization and neural networks. this thesis neural network based methods are investigated to achieve good compression ratios while maintaining the image quality. Parts of this investigation include motion detection, and weight retraining. An adaptive technique is employed to improve the video frame quality for a given compression ratio by frequently updating the weigh
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Zhu, Huaiyu. "Neural networks and adaptive computers : theory and methods of stochastic adaptive computation." Thesis, University of Liverpool, 1993. http://eprints.aston.ac.uk/365/.

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This thesis studies the theory of stochastic adaptive computation based on neural networks. A mathematical theory of computation is developed in the framework of information geometry, which generalises Turing machine (TM) computation in three aspects - It can be continuous, stochastic and adaptive - and retains the TM computation as a subclass called "data processing". The concepts of Boltzmann distribution, Gibbs sampler and simulated annealing are formally defined and their interrelationships are studied. The concept of "trainable information processor" (TIP) - parameterised stochastic mappi
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MacLeod, Christopher. "The synthesis of artificial neural networks using single string evolutionary techniques." Thesis, Robert Gordon University, 1999. http://hdl.handle.net/10059/367.

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The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological techniques are used to grow Artificial Neural Network topologies. The Embryological Algorithm is an alternative to the popular Genetic Algorithm, which is widely used to achieve similar results. The algorithm grows in the sense that the network structure is added to incrementally and thus changes
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Han, Changan. "Neural Network Based Off-line Handwritten Text Recognition System." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/363.

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This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be consid
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Zhang, Yiming. "Applications of artificial neural networks (ANNs) in several different materials research fields." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/362.

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In materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known corr
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Goudarzi, Alireza. "On the Effect of Heterogeneity on the Dynamics and Performance of Dynamical Networks." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/369.

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The high cost of processor fabrication plants and approaching physical limits have started a new wave research in alternative computing paradigms. As an alternative to the top-down manufactured silicon-based computers, research in computing using natural and physical system directly has recently gained a great deal of interest. A branch of this research promotes the idea that any physical system with sufficiently complex dynamics is able to perform computation. The power of networks in representing complex interactions between many parts make them a suitable choice for modeling physical system
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Umbach, Simon Lineu [Verfasser], Jörg [Gutachter] Breitung, and Robinson [Gutachter] Kruse-Becher. "Macroeconomic Forecasting and Evaluation with Supervised and Neural Network Reinforced Factor Models / Simon Lineu Umbach ; Gutachter: Jörg Breitung, Robinson Kruse-Becher." Köln : Universitäts- und Stadtbibliothek Köln, 2021. http://d-nb.info/1236341244/34.

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Smith, Nancy T. "Evolving Credible Facial Expressions with Interactive GAs." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/310.

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A major focus of research in computer graphics is the modeling and animation of realistic human faces. Modeling and animation of facial expressions is a very difficult task, requiring extensive manual manipulation by computer artists. Our primary hypothesis was that the use of machine learning techniques could reduce the manual labor by providing some automation to the process. The goal of this dissertation was to determine the effectiveness of using an interactive genetic algorithm (IGA) to generate realistic variations in facial expressions. An IGA's effectiveness is measured by satisfaction
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Books on the topic "360 neural networks"

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Observed Brain Dynamics. Oxford University Press, USA, 2007.

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Spence, Charles. Orienting Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.015.

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The last 30 years or so have seen a rapid rise in research on attentional orienting from a crossmodal perspective. The majority of this research has tended to focus on the consequences of the covert orienting of attention (either to a sensory modality or spatial location) for both perception and neural information processing. The results of numerous studies have now highlighted the robust crossmodal links that exist in the case of both overt and covert, and both exogenous and endogenous spatial orienting. Neuroimaging studies have started to highlight the neural circuits underlying such crossm
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Book chapters on the topic "360 neural networks"

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Schauer, Carsten, and Horst-Michael Gross. "A Model of Horizontal 360° Object Localization Based on Binaural Hearing and Monocular Vision." In Artificial Neural Networks — ICANN 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_159.

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Raj, Surya, and Ansuman Mahapatra. "Optimizing Deep Neural Network for Viewpoint Detection in 360-Degree Images." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4863-3_49.

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Drisya, S. S., Ansuman Mahapatra, and S. Priyadharshini. "360-Degree Image Classification and Viewport Prediction Using Deep Neural Networks." In Lecture Notes in Networks and Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6_46.

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Sandhu, Harmanjot Singh, Liping Fang, and Ling Guan. "Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method." In Smart City 360°. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33681-7_36.

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Tripathi, Nishith D., Jeffrey H. Reed, and Hugh F. Vanlandingham. "Fuzzy Logic and Neural Networks." In Radio Resource Management in Cellular Systems. Springer US, 2001. http://dx.doi.org/10.1007/0-306-47318-6_2.

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MacKay, D. J. C. "Maximum Entropy Connections: Neural Networks." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3460-6_22.

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Remus, William, and Marcus O’Connor. "Neural Networks for Time-Series Forecasting." In International Series in Operations Research & Management Science. Springer US, 2001. http://dx.doi.org/10.1007/978-0-306-47630-3_12.

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Handa, Nishit, Yash Kaushik, Nikhil Sharma, Muskaan Dixit, and Monika Garg. "Image Classification Using Convolutional Neural Networks." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3660-8_48.

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Gelot, Antoinette. "Ontogenesis of Neuronal Networks." In Advances in Behavioral Biology. Springer US, 2001. http://dx.doi.org/10.1007/0-306-47612-6_6.

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Rodríguez-García, Iñaki, Vicente Pesudo, Roberto Santorelli, and Miguel Cárdenas-Montes. "Neural Networks for Background Rejection in DEAP-3600 Detector." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61705-9_53.

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Conference papers on the topic "360 neural networks"

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Regensky, Andy, Fabian Brand, and André Kaup. "Analysis of Neural Video Compression Networks for 360-Degree Video Coding." In 2024 Picture Coding Symposium (PCS). IEEE, 2024. http://dx.doi.org/10.1109/pcs60826.2024.10566468.

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Mellouk, A., P. Gallinari, and F. Rauscher. "Prediction and discrimination in neural networks for continuous speech recognition." In 3rd European Conference on Speech Communication and Technology (Eurospeech 1993). ISCA, 1993. http://dx.doi.org/10.21437/eurospeech.1993-360.

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Yu, Jiang, and Yong Liu. "Field-of-view prediction in 360-degree videos with attention-based neural encoder-decoder networks." In the 11th ACM Workshop. ACM Press, 2019. http://dx.doi.org/10.1145/3304113.3326118.

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Zhao, Qiang, Chen Zhu, Feng Dai, Yike Ma, Guoqing Jin, and Yongdong Zhang. "Distortion-aware CNNs for Spherical Images." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/167.

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Convolutional neural networks are widely used in computer vision applications. Although they have achieved great success, these networks can not be applied to 360 spherical images directly due to varying distortion effect. In this paper, we present distortion-aware convolutional network for spherical images. For each pixel, our network samples a non-regular grid based on its distortion level, and convolves the sampled grid using square kernels shared by all pixels. The network successively approximates large image patches from different tangent planes of viewing sphere with small local samplin
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Hess, David E., William E. Faller, Robert F. Roddy, Anne M. Pence, and Thomas C. Fu. "Feedforward Neural Networks Applied to Problems in Ocean Engineering." In 25th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/omae2006-92468.

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The Maneuvering and Control Division of the Naval Surface Warfare Center, Carderock Div. (NSWCCD) along with Applied Simulation Technologies have been developing and applying feedforward neural networks (FFNN) to problems of naval interest in Ocean Engineering. A selection of these will be discussed. Together, they show the power of the nonlinear method as well as its utility in diverse applications. Experimental data describing a subset of the B-Screw series of propellers operating in all four quadrants have been reported by MARIN in the Netherlands. The data contain varying pitch to diameter
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Lacombe, Théo, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, and Yuhei Umeda. "Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/367.

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Although neural networks are capable of reaching astonishing performance on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. Being able to monitor the presence of such variations without retraining the network is of crucial importance. In this paper, we develop a method to monitor trained neural networks based on the topological properties of their a
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Kouvaros, Panagiotis, and Alessio Lomuscio. "Towards Scalable Complete Verification of Relu Neural Networks via Dependency-based Branching." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/364.

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We introduce an efficient method for the complete verification of ReLU-based feed-forward neural networks. The method implements branching on the ReLU states on the basis of a notion of dependency between the nodes. This results in dividing the original verification problem into a set of sub-problems whose MILP formulations require fewer integrality constraints. We evaluate the method on all of the ReLU-based fully connected networks from the first competition for neural network verification. The experimental results obtained show 145% performance gains over the present state-of-the-art in com
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He, Yu, Jianxin Li, Yangqiu Song, Mutian He, and Hao Peng. "Time-evolving Text Classification with Deep Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/310.

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Traditional text classification algorithms are based on the assumption that data are independent and identically distributed. However, in most non-stationary scenarios, data may change smoothly due to long-term evolution and short-term fluctuation, which raises new challenges to traditional methods. In this paper, we present the first attempt to explore evolutionary neural network models for time-evolving text classification. We first introduce a simple way to extend arbitrary neural networks to evolutionary learning by using a temporal smoothness framework, and then propose a diachronic propa
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Liu, Hantang, Jialiang Zhang, Jianke Zhu, and Steven C. H. Hoi. "DeepFacade: A Deep Learning Approach to Facade Parsing." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/320.

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The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using boun
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Bian, Shijie, Daniele Grandi, Kaveh Hassani, et al. "Material Prediction for Design Automation Using Graph Representation Learning." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88049.

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Abstract Successful material selection is critical in designing and manufacturing products for design automation. Designers leverage their knowledge and experience to create high-quality designs by selecting the most appropriate materials through performance, manufacturability, and sustainability evaluation. Intelligent tools can help designers with varying expertise by providing recommendations learned from prior designs. To enable this, we introduce a graph representation learning framework that supports the material prediction of bodies in assemblies. We formulate the material selection tas
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Reports on the topic "360 neural networks"

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Xin, Wu, and Xue Tao. The efficacy and safety of neuromodulation in refractory epilepsy: a systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.4.0042.

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Review question / Objective: To assess the efficacy and safety of different neuromodulation applied to the refractory epilepsy and provide a better choice for clinical practice. Condition being studied: Epilepsy is a frequent neurologic illness defined by bursts of hypersynchronized neural network activity that afflict about 1% of the global population. Unfortunately, roughly 30% of people with drug-resistant epilepsy (DRE) continue to experience seizures despite three anti-seizure drugs. In most cases, resective surgery, as the first-line treatment for DRE, is considered a curative therapy fo
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Bodruzzaman, M., and M. A. Essawy. Chaotic behavior control in fluidized bed systems using artificial neural network. Quarterly progress report, April 1, 1996--June 30, 1996. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/410400.

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Bodruzzaman, M. Chaotic behavior monitoring & control in fluidized bed systems using artificial neural network. Quarterly progress report, July 1, 1996--September 30, 1996. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/477756.

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Arhin, Stephen, Babin Manandhar, Kevin Obike, and Melissa Anderson. Impact of Dedicated Bus Lanes on Intersection Operations and Travel Time Model Development. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2040.

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Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the Dist
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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting ther
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