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Record Nr. |
UNINA9910557900103321 |
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Autore |
Kormas Konstantinos Ar |
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Titolo |
Advancing Knowledge on Cyanobacterial Blooms in Freshwaters |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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Descrizione fisica |
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1 online resource (202 p.) |
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Biology, life sciences |
Research & information: general |
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Livello bibliografico |
Monografia |
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Cyanobacterial blooms are a water quality problem that is widely acknowledged to have detrimental ecological and economic effects in drinking and recreational water supplies and fisheries. There is increasing evidence that cyanobacterial blooms have increased globally and are likely to expand in water resources as a result of climate change. Of most concern are cyanotoxins, along with the mechanisms that induce their release and determine their fate in the aquatic environment. These secondary metabolites pose a potential hazard to human health and agricultural and aquaculture products that are intended for animal and human consumption; therefore, strict and reliable control of cyanotoxins is crucial for assessing risk. In this direction, a deeper understanding of the mechanisms that determine cyanobacterial bloom structure and toxin production has become the target of management practices. This Special Issue, entitled "Advancing Knowledge on Cyanobacterial Blooms in Freshwaters", aims to bring together recent multi- and interdisciplinary research, from the field to the laboratory and back again, driven by working hypotheses based on any aspect of mitigating cyanobacterial blooms, from ecological theory to applied research. |
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2. |
Record Nr. |
UNINA9910576871603321 |
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Autore |
Ulhaq Anwaar |
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Titolo |
Advances in Object and Activity Detection in Remote Sensing Imagery |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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Descrizione fisica |
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1 online resource (170 p.) |
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Soggetti |
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History of engineering & technology |
Technology: general issues |
History of engineering and technology |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms. |
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