Journal articles: 'Uncontrolled environments'

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Abbas, Ali, Michael Haslgrübler, Abdul Mannan Dogar, and Alois Ferscha. "Micro Activities Recognition in Uncontrolled Environments." Applied Sciences 11, no. 21 (November 3, 2021): 10327. http://dx.doi.org/10.3390/app112110327.

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Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.

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Hernández-Rabadán, Deny Lizbeth. "Method for Segmenting Tomato Plants in Uncontrolled Environments." Engineering 04, no. 10 (2012): 599–606. http://dx.doi.org/10.4236/eng.2012.410076.

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Martins, Pedro, José Silvestre Silva, and Alexandre Bernardino. "Multispectral Facial Recognition in the Wild." Sensors 22, no. 11 (June 1, 2022): 4219. http://dx.doi.org/10.3390/s22114219.

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This work proposes a multi-spectral face recognition system in an uncontrolled environment, aiming to identify or authenticate identities (people) through their facial images. Face recognition systems in uncontrolled environments have shown impressive performance improvements over recent decades. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The proposed work uses the scores obtained by face recognition systems in different spectral bands to make a joint final decision in identification. The evaluation of different methods for each of the components of a face recognition system allowed the most suitable ones for a multi-spectral face recognition system in an uncontrolled environment to be selected. The experimental results, expressed in Rank-1 scores, were 99.5% and 99.6% in the TUFTS multi-spectral database with pose variation and expression variation, respectively, and 100.0% in the CASIA NIR-VIS 2.0 database, indicating that the use of multi-spectral images in an uncontrolled environment is advantageous when compared with the use of single spectral band images.

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Shafait, Faisal, Ajmal Mian, Mark Shortis, Bernard Ghanem, Phil F. Culverhouse, Duane Edgington, Danelle Cline, Mehdi Ravanbakhsh, James Seager, and Euan S. Harvey. "Fish identification from videos captured in uncontrolled underwater environments." ICES Journal of Marine Science: Journal du Conseil 73, no. 10 (July 18, 2016): 2737–46. http://dx.doi.org/10.1093/icesjms/fsw106.

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Monroy, Javier G., Esteban J. Palomo, Ezequiel López-Rubio, and Javier Gonzalez-Jimenez. "Continuous chemical classification in uncontrolled environments with sliding windows." Chemometrics and Intelligent Laboratory Systems 158 (November 2016): 117–29. http://dx.doi.org/10.1016/j.chemolab.2016.08.011.

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Gunawan, Alexander Agung Santoso, and Reza A. Prasetyo. "Face Recognition Performance in Facing Pose Variation." CommIT (Communication and Information Technology) Journal 11, no. 1 (August 1, 2017): 1. http://dx.doi.org/10.21512/commit.v11i1.1847.

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There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.

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Edwards, Dalton J., and Logan T. Trujillo. "An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States." Brain Sciences 11, no. 3 (March 5, 2021): 330. http://dx.doi.org/10.3390/brainsci11030330.

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Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

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Botero-Valencia, Juan, Luis Castano-Londono, and David Marquez-Viloria. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments." Data 7, no. 6 (June 16, 2022): 81. http://dx.doi.org/10.3390/data7060081.

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The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository.

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Hum, Yan Chai, Yee Kai Tee, Wun-She Yap, Hamam Mokayed, Tian Swee Tan, Maheza Irna Mohamad Salim, and Khin Wee Lai. "A contrast enhancement framework under uncontrolled environments based on just noticeable difference." Signal Processing: Image Communication 103 (April 2022): 116657. http://dx.doi.org/10.1016/j.image.2022.116657.

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Barcelo-Ordinas, Jose M., Messaoud Doudou, Jorge Garcia-Vidal, and Nadjib Badache. "Self-calibration methods for uncontrolled environments in sensor networks: A reference survey." Ad Hoc Networks 88 (May 2019): 142–59. http://dx.doi.org/10.1016/j.adhoc.2019.01.008.

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Liu, Dianting, Yilin Yan, Mei-Ling Shyu, Guiru Zhao, and Min Chen. "Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments." International Journal of Multimedia Data Engineering and Management 6, no. 1 (January 2015): 1–18. http://dx.doi.org/10.4018/ijmdem.2015010101.

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Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.

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Lopez-Rincon, Omar, Oleg Starostenko, Vicente Alarcon-Aquino, and Juan C. Galan-Hernandez. "Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments." Journal of Electrical and Computer Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4613628.

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Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

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Schindler, David, Sascha Spors, Burcu Demiray, and Frank Krüger. "Automatic Behavior Assessment from Uncontrolled Everyday Audio Recordings by Deep Learning." Sensors 22, no. 22 (November 8, 2022): 8617. http://dx.doi.org/10.3390/s22228617.

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The manual categorization of behavior from sensory observation data to facilitate further analyses is a very expensive process. To overcome the inherent subjectivity of this process, typically, multiple domain experts are involved, resulting in increased efforts for the labeling. In this work, we investigate whether social behavior and environments can automatically be coded based on uncontrolled everyday audio recordings by applying deep learning. Recordings of daily living were obtained from healthy young and older adults at randomly selected times during the day by using a wearable device, resulting in a dataset of uncontrolled everyday audio recordings. For classification, a transfer learning approach based on a publicly available pretrained neural network and subsequent fine-tuning was implemented. The results suggest that certain aspects of social behavior and environments can be automatically classified. The ambient noise of uncontrolled audio recordings, however, poses a hard challenge for automatic behavior assessment, in particular, when coupled with data sparsity.

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Sugiyama, Yuki, Kohei Uno, and Yusuke Matsui. "Types of anomalies in two-dimensional video-based gait analysis in uncontrolled environments." PLOS Computational Biology 19, no. 1 (January 19, 2023): e1009989. http://dx.doi.org/10.1371/journal.pcbi.1009989.

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Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large populations. However, it is considered difficult to completely homogenize the environment and settings during the measurement of large populations. Therefore, it is necessary to appropriately deal with technical errors that are not related to the biological factors of interest. In this study, by analyzing a large cohort database, we have identified four major types of anomalies that occur during gait analysis using OpenPose in uncontrolled environments: anatomical, biomechanical, and physical anomalies and errors due to estimation. We have also developed a workflow for identifying and correcting these anomalies and confirmed that this workflow is reproducible through simulation experiments. Our results will help obtain a comprehensive understanding of the anomalies to be addressed during pre-processing for 2D video-based gait analysis of large populations.

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S, Venkatramaphanikumar, and K. V. Krishna Kishore. "Face Recognition with Modular Two Dimensional PCA under Uncontrolled Illumination Variations." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (August 1, 2016): 1610. http://dx.doi.org/10.11591/ijece.v6i4.9761.

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Person authenticaton using faces became one of the most popular security approaches for the last three decades. From the literature it is found that perofrmance of most of the methods used in recognition was limited due to uncontrolled conditions like illumination and pose variations. In this work, to address the limitations of uncontrolled environment, Modular two-dimensional Principle Component Analysis (M2D-PCA) is proposed. In this approach, the input image is partitioned into four equal segments and then Histogram Equalization is applied to reduce illumination impact caused due to varying lightening conditions. Then M2D-PCA algorithm is applied parallel on each segment and then all features extracted from the segments are fused with wieghted summation. Experiments are carried out on bench mark datasets like extended Yale database B, ORL and AR database. Results of the proposed approach produced good recognition rate with low computational time against various illumination environments.

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S, Venkatramaphanikumar, and K. V. Krishna Kishore. "Face Recognition with Modular Two Dimensional PCA under Uncontrolled Illumination Variations." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (August 1, 2016): 1610. http://dx.doi.org/10.11591/ijece.v6i4.pp1610-1616.

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Person authenticaton using faces became one of the most popular security approaches for the last three decades. From the literature it is found that perofrmance of most of the methods used in recognition was limited due to uncontrolled conditions like illumination and pose variations. In this work, to address the limitations of uncontrolled environment, Modular two-dimensional Principle Component Analysis (M2D-PCA) is proposed. In this approach, the input image is partitioned into four equal segments and then Histogram Equalization is applied to reduce illumination impact caused due to varying lightening conditions. Then M2D-PCA algorithm is applied parallel on each segment and then all features extracted from the segments are fused with wieghted summation. Experiments are carried out on bench mark datasets like extended Yale database B, ORL and AR database. Results of the proposed approach produced good recognition rate with low computational time against various illumination environments.

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Valderrama, Wendy, Andrea Magadan, Osslan Osiris Vergara, Jose Ruiz, Raul Pinto, and Gerardo Reyes. "Detection of Facial Spoofing Attacks in Uncontrolled Environments Using ELBP and Color Models." IEEE Latin America Transactions 20, no. 6 (June 2022): 875–83. http://dx.doi.org/10.1109/tla.2022.9757369.

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Curteis, Tobit, Lucy Wrapson, and Janet Berry. "East Anglia’s Medieval Rood Screens: Conserving Sensitive Painted Artworks in Uncontrolled Church Environments." Studies in Conservation 65, sup1 (April 22, 2020): P54—P59. http://dx.doi.org/10.1080/00393630.2020.1752970.

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Vural, Sadi, Yasushi Mae, Huseyin Uvet, and Tatsuo Arai. "Multi-view fast object detection by using extended haar filters in uncontrolled environments." Pattern Recognition Letters 33, no. 2 (January 2012): 126–33. http://dx.doi.org/10.1016/j.patrec.2011.10.004.

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Hernández-Rabadán, Deny Lizbeth, Fernando Ramos-Quintana, and Julian Guerrero Juk. "Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/214674.

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This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups usingK-means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.

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Chakroun, Rania, and Mondher Frikha. "Efficient text-independent speaker recognition with short utterances in both clean and uncontrolled environments." Multimedia Tools and Applications 79, no. 29-30 (May 3, 2020): 21279–98. http://dx.doi.org/10.1007/s11042-020-08824-7.

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Lidon, Fernando C., Ana S. Almeida, Ana R. Costa, Ana S. Bagulho, Paula Scotti-Campos, José N. Semedo, Benvindo Maçãs, et al. "Sequential zinc and iron biofortification of bread-wheat grains: from controlled to uncontrolled environments." Crop and Pasture Science 66, no. 11 (2015): 1097. http://dx.doi.org/10.1071/cp14270.

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The development of knowledge on bread wheat (Triticum aestivum L.) biofortification in zinc (Zn) and iron (Fe), related to its potential agronomical use and the nutritional and technological implications, is becoming important to strategies for improving human nutrition. In this context, we studied the accumulation of Zn and Fe in grains, considering potential uptake and translocation kinetics, photoassimilate production and deposition, and related yields, in grains of cv. Roxo produced under controlled-environment conditions and used thereafter in field trials. The metabolic plasticity of this wheat genotype grown under controlled-environment conditions allowed a 10- and 4-fold enhancement in accumulation of Zn and Fe in the grains after nutrient supplementation with a 5-fold concentrated Hoagland solution (5S), after two generations. Moreover, when these seeds were sown under field conditions and the resulting plants supplemented with or without Zn and Fe, the accumulation of these nutrients decreased within the next two generations. Such field seeds obtained without further Zn and Fe supplementation (with nitrogen only; F3(S) and F4(S)) maintained enhanced levels of Zn (~400%) and Fe (40–50%) compared with the initial seeds. If Zn and Fe supplement was given to the plants germinated from F2(5S), the subsequent F3(5S) and F4(5S) seeds maintained the Zn increase (~400%), whereas a further enhancement was observed for Fe, to 75% and 89%, respectively. Toxic limits were not reached for photosynthetic functioning. Even under the highest Zn and Fe supplement dose given to the F3(5S) plants, there was only a slight effect on photosystem II photochemical performance; in fact, enhanced net photosynthesis values were observed. In conclusion, within this experimental design, Zn and Fe biofortification can be obtained without toxicity effects on photosynthetic performance and with negligible modifications to grain texture and nutritional value (protein quality and contents as well as fatty acids).

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Ruiz, Leandro, Manuel Torres, Alejandro Gómez, Sebastián Díaz, José M. González, and Francisco Cavas. "Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network." Applied Sciences 10, no. 19 (September 29, 2020): 6856. http://dx.doi.org/10.3390/app10196856.

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The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

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Clarke, Peter G. H. "The limits of brain determinacy." Proceedings of the Royal Society B: Biological Sciences 279, no. 1734 (February 2012): 1665–74. http://dx.doi.org/10.1098/rspb.2011.2629.

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The genes do not control everything that happens in a cell or an organism, because thermally induced molecular movements and conformation changes are beyond genetic control. The importance of uncontrolled events has been argued from the differences between isogenic organisms reared in virtually identical environments, but these might alternatively be attributed to subtle, undetected differences in the environment. The present review focuses on the uncontrolled events themselves in the context of the developing brain. These are considered at cellular and circuit levels because even if cellular physiology was perfectly controlled by the genes (which it is not), the interactions between different cells might still be uncoordinated. A further complication is that the brain contains mechanisms that buffer noise and others that amplify it. The final resultant of the battle between these contrary mechanisms is that developmental stochasticity is sufficiently low to make neurobehavioural defects uncommon, but a chance component of neural development remains. Thus, our brains and behaviour are not entirely determined by a combination of genes-plus-environment.

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Zhao, Changrong, Zhang Ting, Zhaoyang You, Hyunook Kim, and Kinjal J. Shah. "Uncontrolled Disposal of Used Masks Resulting in Release of Microplastics and Co-Pollutants into Environment." Water 14, no. 15 (August 2, 2022): 2403. http://dx.doi.org/10.3390/w14152403.

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The global panic caused by COVID-19 has continued to increase people’s demand for masks. However, due to inadequate management and disposal practice, these masks have, unfortunately, entered the environment and release a large amount of microplastics (MPs), posing a serious threat to the environment and human health. Understanding the occurrence of mask waste in various environments, release of mask-origin MPs, and related environmental risk is essential to mask-waste management in current and future epidemic prevention and control. This paper focuses on the global distribution of mask waste, the potential release of waste-origin MPs, and the impact on the environment. Specifically, the physical and chemical properties of polypropylene (the most common plastic material in a mask), which show a high adsorption capacity for heavy metals and organic pollutants and play a role as a support for microbial growth, were extensively reported. In addition, several important issues that need to be resolved are raised, which offers a direction for future research. This review focuses on the essentiality of handling masks to avoid potential environmental issues.

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Giasson, Larissa Aparecida, Adriana Paula D’Agostini Contreiras-Rodrigues, Daniela Aparecida Dalla-Costa, Betania Brum de Bortolli, Aline Ferreira Coelho, and Silvia Rahe Pereira. "Physiological quality and seed storage potential of four soybean cultivars." Research, Society and Development 10, no. 4 (March 31, 2021): e7910413635. http://dx.doi.org/10.33448/rsd-v10i4.13635.

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The objective of this study was to evaluate physiological seeds quality of four soybean cultivars during storage, under controlled and uncontrolled conditions. Seeds of Brasmax Lança IPRO, Brasmax Raio IPRO (está diferente do Material e Médtodos), Pioneer 95y52 and TMG 7062 IPRO cultivars were stored in controlled (20 ± 1.2 °C) and uncontrolled (producer's warehouse) environments and the following traits were assessed: percentage of germination (GERM), germination speed index (GSI), germination speed (GS) and accelerated aging (AA) at 0, 60, 120 and 180 days of storage. Covariance matrices structure of storage time was tested through repeated measures analysis, adopting a bifactorial model. Data were submitted to ANOVA (α=5%) and when the means presented significant differences, regression analysis and Scott-Knott test (α=5%) were performed. Differences in soybean seeds physiological quality are not related to storage environment. Lança cultivar was the most tolerant to storage, while Ray and TMG 7062 cultivars were more sensitive.

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Derbyshire, Alan, and Tatjana Kecojevic. "THE BOKA KOTORSKA: A VERNACULAR RESPONSE TO SUSTAINABLE URBAN ENVIRONMENTS." JOURNAL OF ARCHITECTURE AND URBANISM 38, no. 2 (July 8, 2014): 148–60. http://dx.doi.org/10.3846/20297955.2014.925630.

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This study focuses on the development of the Boka Kotorska region of Montenegro. As such it attempts to analyse the viability of sustainable development in the region and considers the role of vernacular architecture and ecology as contributory factors. The development of the tourist industry within Montenegro is a key element within the overall strategy for economic growth. This has obvious implications for the urban development of the case study area as uncontrolled deregulated expansion threatens the future viability of attempts to create genuine environmentally sensitive planning policies. Accordingly, this study raises and discusses many of the issues faced by stakeholders in implementing innovative spatial and architectural development.

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Lin, Tzu-Jui, and Karl A. Stol. "Autonomous Surveying of Plantation Forests Using Multi-Rotor UAVs." Drones 6, no. 9 (September 16, 2022): 256. http://dx.doi.org/10.3390/drones6090256.

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Modern plantation forest procedures still rely heavily on manual data acquisition in the inventory process, limiting the quantity and quality of the collected data. This limitation in collection performance is often due to the difficulty of traversing the plantation forest environment on foot. This work presents an autonomous system for exploring plantation forest environments using multi-rotor UAVs. The proposed method consists of three parts: waypoint selection, trajectory generation, and trajectory following. Waypoint selection is accomplished by estimating the rows’ locations within the environment and selecting points between adjacent rows. Trajectory generation is completed using a non-linear optimization-based constant speed planner and the following is accomplished using a model predictive control approach. The proposed method is tested extensively in simulation against various procedurally generated forest environments, with results suggesting that it is robust against variations within the scene. Finally, flight testing is performed in a local plantation forest, demonstrating the successful application of our proposed method within a complex, uncontrolled environment.

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Marques, Elizabeth Rosemeire, Eduardo Fontes Araújo, Roberto Fontes Araújo, Sebastião Martins Filho, and Plínio César Soares. "Seed quality of rice cultivars stored in different environments." Journal of Seed Science 36, no. 1 (2014): 32–39. http://dx.doi.org/10.1590/s2317-15372014000100004.

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The objective of this study was to evaluate dormancy and physiological quality of seeds of rice cultivars during storage in different environments. After harvesting, the seeds of three rice cultivars (Seleta, Curinga and Relâmpago) were dried in the sun, to reach moisture content at around 13%. Then, they were packed in paper and stored in four environments: 5 ± 2 ºC / 70 ± 5% RH, 12 ± 2 ºC / 70 ± 5% RH, 18 ± 2 ºC / 65 ± 5% RH and in uncontrolled condition of temperature and relative humidity (natural). Physiological quality was evaluated at the beginning and at 3, 6, 9 and 12 months of storage by germination test, electrical conductivity, accelerated aging and sand emergence. The experiment was conducted in split plots in a completely randomized design with three replications. Environmental factor was applied in the plots, cultivars in the subplots and storage period in subplots. Cultivar Seleta showed higher dormancy, which was surpassed during storage regardless of environment conservation. In general, seeds stored in natural environment showed lower physiological quality. Only the seeds of cultivar Seleta, regardless of the environment, maintained germination above the minimum required for commercialization until six months of storage.

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van Iersel, Marc W. "(295) Measuring and Reporting Growing Conditions in Controlled Environments." HortScience 40, no. 4 (July 2005): 1009B—1009. http://dx.doi.org/10.21273/hortsci.40.4.1009b.

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Do you accurately measure and report the growing conditions of your controlled environment experiments? Conditions in controlled environment plant growth rooms and chambers should be reported in detail. This is important to allow replication of experiments on plants, to compare results among facilities, and to avoid artefacts due to uncontrolled variables. The International Committee for Controlled Environment Guidelines, with representatives from the U.K. Controlled Environment Users' Group, the North American Committee on Controlled Environment Technology and Use (NCR-101), and Australasian Controlled Environment Working Group (ACEWG), has developed guidlines to report environmental conditions in controlled environment experiments. These guidelines include measurements of light, temperature, humidity, CO2, air speed, and fertility. A brochure with these guidelines and a sample paragraph on how to include this information in a manuscript will be available.

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Peng, En, and Ling Li. "Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments." Pattern Recognition 43, no. 3 (March 2010): 1188–98. http://dx.doi.org/10.1016/j.patcog.2009.08.003.

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Evans, Alex, and Amy Knehans. "Worldwide need for a comprehensive database of stability data for pharmaceuticals stored in uncontrolled environments." Journal of Public Health Policy 34, no. 3 (May 2, 2013): 477–80. http://dx.doi.org/10.1057/jphp.2013.16.

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Lopes, Arinos Alves da Silva, Jessica Lemos Gomes, Lays Carolline Souza, and Jaqueline Maria da Silva. "MODELAGEM DO COMPORTAMENTO DISPERSIVO DE MATERIAL IMPACTANTE EM UM LAGO POR MEIO DA EQUAÇÃO DE DIFUSÃO-ADVECÇÃO." Ciência e Natura 38, no. 2 (May 31, 2016): 756. http://dx.doi.org/10.5902/2179460x19037.

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The exponential and uncontrolled growth of the world population can affect the population’s quality of life and lead to various environment and health problems, such as deforestation to make way for new housing and an increase in the water supply demand and in the amount of pollutants released in the water bodies. Several mathematical models have been developed to try to understand and solve problems of dispersion of pollutants in aquatic environments. This article aims to study, analyze and model the dispersion behavior of contaminants and pollutants in a lake using the Diffusion-Advection differential equations model.

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Sorano, Leticia Almeida, Maycom Dias de Lima, Grazieli Suszek, and Ana Flávia Basso Royer. "PRECISION POULTRY FARMING: MAPPING ATTRIBUTES THAT INFLUENCE LAYING BIRDS IN NOVA ANDRADINA/MS." JOURNAL OF NEOTROPICAL AGRICULTURE 7, no. 4 (November 30, 2020): 15–22. http://dx.doi.org/10.32404/rean.v7i4.4961.

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Precision poultry farming is related to effective monitoring of production stages and product quality. This work evaluated the influence of temperature, air humidity, and luminosity variables on chicken egg production and quality (internal and external), under uncontrolled environment and natural lighting, using precision poultry techniques. One hundred and sixty birds of Embrapa 51 line, reared in cages, were assessed with 24, 30, 50, and 70 weeks of life. Values of temperature, relative humidity, luminosity, and the samples for productivity and egg quality determination were collected in predefined locations in a poultry house belonging to IFMS, Nova Andradina/MS Campus. Spatial maps were generated using the inverse distance weighted interpolation (IDW). Environmental variables show significant correlations with production and internal/external quality parameters of chicken egg raised in uncontrolled environments. The use of spatial maps for environmental characteristics of the poultry house, production, and internal/external chicken egg quality provides better visualization of the variations and correlations among the investigated variables, assisting the process of decision-making related to appropriate management and optimization of the poultry house.

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Brenes-Torres, Juan Carlos, Francisco Blanes, and José Simo. "Magnetic Trails: A Novel Artificial Pheromone for Swarm Robotics in Outdoor Environments." Computation 10, no. 6 (June 15, 2022): 98. http://dx.doi.org/10.3390/computation10060098.

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Swarm robotics finds inspiration in nature to model behaviors, such as the use of pheromone principles. Pheromones provide an indirect and decentralized communication scheme that have shown positive experimental results. Real implementations of pheromones have suffered from slow sensors and have been limited to controlled environments. This paper presents a novel technology to implement real pheromones for swarm robotics in outdoor environments by using magnetized ferrofluids. A ferrofluid solution, with its deposition and magnetization system, is detailed. The proposed substance does not possess harmful materials for the environment and can be safely handled by humans. Validation demonstrates that the substance represents successfully pheromone characteristics of locality, diffusion and evaporation on several surfaces in outdoor conditions. Additionally, the experiments show an improvement over the chemical representation of pheromones by using magnetic substances and existing magnetometer sensor technologies, which provide better response rates and recovery periods than MOX chemical sensors. The present work represents a step toward swarm robotics experimentation in uncontrolled outdoor environments. In addition, the presented pheromone technology may be use by the broad area of swarm robotics for robot exploration and navigation.

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Sanches, S. R. R., R. Nakamura, V. F. da Silva, and R. Tori. "Bilayer Segmentation of Live Video in Uncontrolled Environments for Background Substitution: An Overview and Main Challenges." IEEE Latin America Transactions 10, no. 5 (September 2012): 2138–49. http://dx.doi.org/10.1109/tla.2012.6362359.

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Baek, Na Rae, Se Woon Cho, Ja Hyung Koo, and Kang Ryoung Park. "Pedestrian Gender Recognition by Style Transfer of Visible-Light Image to Infrared-Light Image Based on an Attention-Guided Generative Adversarial Network." Mathematics 9, no. 20 (October 9, 2021): 2535. http://dx.doi.org/10.3390/math9202535.

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Gender recognition of pedestrians in uncontrolled outdoor environments, such as intelligent surveillance scenarios, involves various problems in terms of performance degradation. Most previous studies on gender recognition examined recognition methods involving faces, full body images, or gaits. However, the recognition performance is degraded in uncontrolled outdoor environments due to various factors, including motion and optical blur, low image resolution, occlusion, pose variation, and changes in lighting. In previous studies, a visible-light image in which image restoration was performed and infrared-light (IR) image, which is robust to the type of clothes, accessories, and lighting changes, were combined to improve recognition performance. However, a near-IR (NIR) image requires a separate NIR camera and NIR illuminator, because of which challenges are faced in providing uniform illumination to the object depending on the distance to the object. A thermal camera, which is also called far-IR (FIR), is not widely used in a surveillance camera environment because of expensive equipment. Therefore, this study proposes an attention-guided GAN for synthesizing infrared image (SI-AGAN) for style transfer of visible-light image to IR image. Gender recognition performance was improved by using only a visible-light camera without an additional IR camera by combining the synthesized IR image obtained by the proposed method with the visible-light image. In the experiments conducted using open databases—RegDB database and SYSU-MM01 database—the equal error rate (EER) of gender recognition of the proposed method in each database was 9.05 and 12.95%, which is higher than that of state-of-the-art methods.

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Tamilselvi M. and S. Karthikeyan. "Hybrid Framework for a Robust Face Recognition System Using EVB_CNN." Journal of Cases on Information Technology 23, no. 3 (July 2021): 43–57. http://dx.doi.org/10.4018/jcit.20210701.oa4.

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Recognition of the human face is becoming an ingenious technology that enhancing its strategy gradually by finding its applications in a wide variety of fields including security and surveillance. The traditional methods that are in practise for face recognition are not adequate in producing good accuracy due to two main reasons. The first one is the pictures are affected by various uncontrolled situations such as illumination, blur, and pose, and the second one is struggling in an efficient recognition when dealing with a large number of samples. There is need for an effective face recognition as a part of life in the automated environment. The traditional methods are lagging with some parameters. To overcome the aforementioned issues, a new methodology is implemented. This methodology is a hybrid frame work combined with Eigen value-based convolutional neural networks (EVB_CNN). The EVB_CNN is designed in such a way that the significant features are extracted and classified by the softmax function and fully connected layer, respectively. The experimental analysis is carried out with AR data set and ORL data set that shows enhancement in accuracy with significant reduction in computation time with images taken over specific uncontrolled environments.

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Han, Wenli, and Shige Wang. "Advances in Hemostatic Hydrogels That Can Adhere to Wet Surfaces." Gels 9, no. 1 (December 22, 2022): 2. http://dx.doi.org/10.3390/gels9010002.

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Currently, uncontrolled bleeding remains a serious problem in emergency, surgical and battlefield environments. Despite the specific properties of available hemostatic agents, sealants, and adhesives, effective hemostasis under wet and dynamic conditions remains a challenge. In recent years, polymeric hydrogels with excellent hemostatic properties have received much attention because of their adjustable mechanical properties, high porosity, and biocompatibility. In this review, to investigate the role of hydrogels in hemostasis, the mechanisms of hydrogel hemostasis and adhesion are firstly elucidated, the adhesion design strategies of hemostatic hydrogels in wet environments are briefly introduced, and then, based on a comprehensive literature review, the studies and in vivo applications of wet-adhesive hemostatic hydrogels in different environments are summarized, and the improvement directions of such hydrogels in future studies are proposed.

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Reggente, Matteo, and Achim J. Lilienthal. "Statistical Evaluation of the Kernel DM+V/W Algorithm for Building Gas Distribution Maps in Uncontrolled Environments." Procedia Chemistry 1, no. 1 (September 2009): 481–84. http://dx.doi.org/10.1016/j.proche.2009.07.120.

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de Mello, Juliana Lolli Malagoli, Mariana Piatto Berton, Rita de Cassia Dourado, Aline Giampietro-Ganeco, Rodrigo Alves de Souza, Fábio Borba Ferrari, Pedro Alves de Souza, and Hirasilva Borba. "Physical and chemical characteristics of the longissimus dorsi from swine reared in climate-controlled and uncontrolled environments." International Journal of Biometeorology 61, no. 10 (April 20, 2017): 1723–31. http://dx.doi.org/10.1007/s00484-017-1354-9.

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Ferrera, Eduardo, Alfonso Alcántara, Jesús Capitán, Angel Castaño, Pedro Marrón, and Aníbal Ollero. "Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments." Sensors 18, no. 12 (November 23, 2018): 4101. http://dx.doi.org/10.3390/s18124101.

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The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.

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Klein, Devorah E., and Matthew J. Jordan. "Methods of Assessing Medical Devices." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 23 (September 2002): 1890–94. http://dx.doi.org/10.1177/154193120204602305.

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While designing and validating any complex system has challenges, the medical domain has specific requirements which must be considered for a system or device to be successful. The environments, communities of use, and interactions are varied, unpredictable, uncontrolled, and ever-changing. Given the environments, communities of use, and interactions involved with medical devices, successful early and late validation of the device must be informed by the context of use itself. Building “frameworks” which represent the context of use for the device can focus validation goals, methods, and criteria and ensure that validation is directed and appropriate. In this paper we present a process and associated methods for defining the frameworks in which medical devices can be successfully assessed. The phases of the process include Phase1: Definition in which a framework of understanding is built which represents the environment of use, community of users, and the interactions between systems and users for the medical device in development. In Phase 2: Validation the framework which defines the environment of use, community of users, and the interactions between systems and users is used to develop a validation approach and criteria. The developing device is then validated against the framework itself.

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Anjos, Alisson Pereira, Francisco Assis da Silva, Leandro Luiz de Almeida, Danillo Roberto Pereira, Mário Augusto Pazoti, Almir Olivette Artero, and Marco Antonio Piteri. "RECONHECIMENTO DE CÉDULAS DO REAL A PARTIR DE IMAGENS USANDO CNN PARA AUXILIAR DEFICIENTES VISUAIS." COLLOQUIUM EXACTARUM 13, no. 1 (September 13, 2021): 77–88. http://dx.doi.org/10.5747/ce.2021.v13.n1.e352.

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Real Banknotes recognition through touch has always been a problem found by visually impaired. The advancement of technology makes it possible to solve this problem computationally. In this work, we present a method to perform Real Banknotes recognition from images using computational vision and artificial intelligence algorithms. The results show that computational cost and recognitionrate are acceptable for use in uncontrolled environments. The processing time for recognition of each Real banknote was 200 milliseconds, with an accuracy of 91.67%.

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Park, Sunyeap, and Yonghwan Jeong. "Proactive Motion Planning for Uncontrolled Blind Intersections to Improve the Safety and Traffic Efficiency of Autonomous Vehicles." Applied Sciences 12, no. 22 (November 14, 2022): 11570. http://dx.doi.org/10.3390/app122211570.

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For the last two decades, autonomous vehicles have been proposed and developed to extend the operational design domain from the motorway to urban environments. However, there have been few studies on autonomous driving for uncontrolled and blind intersections. This paper presents a proactive motion planning algorithm to enhance safety and traffic efficiency simultaneously for autonomous driving in uncontrolled blind intersections. The target states of approach motion are decided based on the field of view of the laser scanner and the pre-defined intersection map with connectivity information. The model predictive controller is used to follow the target states and determine the longitudinal motion of an autonomous vehicle. A Monte Carlo simulation with a case study was conducted to evaluate the performance of the proposed proactive motion planner. The simulation results show that the risk caused by approaching vehicles from the occluded region is properly managed. In addition, the traffic flow is improved by reducing the required time to cross the intersections.

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Li, Thomas S. C., K. E. Bedford, and P. L. Sholberg. "Improved Germination of American Ginseng Seeds Under Controlled Environments." HortTechnology 10, no. 1 (January 2000): 131–35. http://dx.doi.org/10.21273/horttech.10.1.131.

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Traditionally, American ginseng (Panax quinquefolium L.) seeds are stratified for 18 to 22 months, before seeding, in a sandbox buried outdoors in late August or early September. Uncontrolled fluctuating temperature and moisture levels and the presence of pathogenic organisms in the seed box can cause seeds to sprout prematurely, rot, dry out and die. A study was initiated to shorten the lengthy stratification period, and to increase seed viability and percentage of germination by stratifying seeds indoors under a controlled environment. Seeds were subjected to various periods of warm [15 or 20 °C (59 or 68 °F)] and cold [2 °C (35.6 °F)] temperature stratification regimes in growth chambers. Embryo growth and viability, and seed moisture content were tested periodically during stratification. The best warm regime for embryo development, seed viability and germination after subsequent cold treatment was 15 °C (59 °F). The first “split” seeds, indicating incipient germination, were observed after 3 months of warm [15 °C (59 °F)] and 4 months of cold [2 °C (35.6 °F)] treatment, when average embryo length reached 6 mm (0.24 inch). Greenhouse germination of stratified seeds was as high as 80%. The results from this study indicate that good germination is possible when ginseng seeds are stratified indoors under a controlled environment and seeds can be made to germinate at any time of the year.

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Mitchell, Cary A. "History of Controlled Environment Horticulture: Indoor Farming and Its Key Technologies." HortScience 57, no. 2 (February 2022): 247–56. http://dx.doi.org/10.21273/hortsci16159-21.

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The most recent platform for protected horticultural crop production, with the shortest history to date, is located entirely indoors, lacking even the benefit of free, natural sunlight. Although this may not sound offhand like a good idea for commercial specialty-crop production, the concept of indoor controlled-environment plant growth started originally for the benefit of researchers—to systematically investigate effects of specific environmental factors on plant growth and development in isolation from environmental factors varying in uncontrolled ways that would confound or change experimental findings. In addition to its value for basic and applied research, it soon was discovered that providing nonlimiting plant-growth environments greatly enhanced crop yield and enabled manipulation of plant development in ways that were never previously possible. As supporting technology for indoor crop production has improved in capability and efficiency, energy requirements have declined substantially for growing crops through entire production cycles in completely controlled environments, and this combination has spawned a new sector of the controlled-environment crop-production industry. This article chronicles the evolution of events, enabling technologies, and entrepreneurial efforts that have brought local, year-round indoor crop production to the forefront of public visibility and the threshold of profitability for a growing number of specialty crops in locations with seasonal climates.

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Bockholt, Susanne M., J. Paige West, and Walter E. Bollenbacher. "Cancer Cell Biology: A Student-Centered Instructional Module Exploring the Use of Multimedia to Enrich Interactive, Constructivist Learning of Science." Cell Biology Education 2, no. 1 (March 2003): 35–50. http://dx.doi.org/10.1187/cbe.02-08-0033.

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Multimedia has the potential of providing bioscience education novel learning environments and pedagogy applications to foster student interest, involve students in the research process, advance critical thinking/problem-solving skills, and develop conceptual understanding of biological topics. Cancer Cell Biology, an interactive, multimedia, problem-based module, focuses on how mutations in protooncogenes and tumor suppressor genes can lead to uncontrolled cell proliferation by engaging students as research scientists/physicians with the task of diagnosing the molecular basis of tumor growth for a group of patients. The process of constructing the module, which was guided by scientist and student feedback/responses, is described. The completed module and insights gained from its development are presented as a potential “multimedia pedagogy” for the development of other multimedia science learning environments.

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Pérez-Fuentes, María, María Molero Jurado, María Simón Márquez, and José Gázquez Linares. "The Reasons for Doing Physical Exercise Mediate the Effect of Self-Esteem on Uncontrolled Eating Amongst Nursing Personnel." Nutrients 11, no. 2 (January 31, 2019): 302. http://dx.doi.org/10.3390/nu11020302.

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Background: Since the beginning of the 20th century, the importance of creating healthy work environments and promoting the health of workers in the healthcare sector to create Healthy and Resilient Organizations has been emphasized. In this context, self-esteem is an essential construct which influences health and healthy life styles, and, therefore, the general wellbeing of nurses. The objective of this study was to analyze the mediating role of reasons for exercising in the effect that self-esteem has on uncontrolled eating by nursing professionals. Methods: The sample was made up of 1094 nurses who were administered the Rosenberg General Self-Esteem Scale, the Goal Content for Exercise Questionnaire, and the Three-Factor Eating Questionnaire-R18. Results: Bivariate correlation analysis and multiple mediation analysis showed that self-esteem has direct and indirect effects on uncontrolled eating. Moreover, self-esteem determines whether one does physical exercise to improve one’s image, recognition, or social affiliation—although the effects on uncontrolled eating were only significant in the case of image. Conclusions: The results have important practical implications in the framework of Positive Occupational Health Psychology (POHP) as they emphasize self-esteem, physical exercise and eating as essential aspects of the health and wellbeing of employees in the healthcare sector, highlighting the importance of creating organizations committed to promoting the psychosocial health of their workers.

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Lucà, Francescantonio, Stefano Manzoni, Alfredo Cigada, Silvia Barella, Andrea Gruttadauria, and Francesco Cerutti. "Automatic Detection of Real Damage in Operating Tie-Rods." Sensors 22, no. 4 (February 10, 2022): 1370. http://dx.doi.org/10.3390/s22041370.

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Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.

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Journal articles on the topic 'Uncontrolled environments'

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