Carbon sequestration service of Mediterranean forest and other wooded land is threatened by their fragile, complex, and highly evolving nature, due to both human disturbances and climate change. ...Remote-sensing methods for forest biomass estimation have gained increased attention, and substantial research has been conducted worldwide over the past four decades. Yet, the literature body focused on Mediterranean forests is rather limited as a result of their small extent compared to other biomes. We discuss the remote-sensing studies over the Mediterranean forest and other wooded land, discriminating research based on the primary data source used, such as optical imagery, datasets from active sensors, and combination of multisource data. The review indicates that there is a significant research gap in terms of the studies, as well as a need for a reduction of the errors and uncertainty of estimates, which are associated with both the sensors' characteristics and the Mediterranean forest and other wooded land structure. Biomass estimates based on optical data were generally less accurate (R
2
close to 0.70, where R
2
is the coefficient of determination), however, when data from active sensors were involved, accuracy of estimations was considerably greater (usually R
2
greater than 0.80). With respect to scale, most of the local scale studies established relationships with R
2
over 0.70 and as high as 0.98, while the few regional scale studies exhibited R
2
close to 0.80. Further, in-depth analysis can provide more efficient data fusion, classification methods, and procedures for operational regional and national assessment of forest biomass over such Mediterranean areas.
Hitherto, the intelligent evaluation of black tea fermentation is still an unsolved problem because it is difficult to obtain the complicated changes information of tea composition, color, texture ...and aroma in the fermentation process at the same time. In this research, hyperspectral imaging technology was used to collect sensory information including taste (sample spectra), vision (sample color image) and olfactory (pH, porphyrin and metalloporphyrin (TPP) sensing array spectra) of fermentation leaves. Subsequently, different data fusion strategies combined with support vector machine algorithm (SVM) were used to establish the fermentation degree discrimination model. The performance of the established models using data fusion strategy were better than that of the model using each single information. The middle-level-PCA strategy achieved a satisfactory performance, with the variable compression rate of 99% and the accuracy of 95% for the prediction set. Remarkably, for the most important moderate fermentation class, the precision and recall of the model were 100% both in calibration and prediction set. These results demonstrated that our proposed strategy could accurately evaluate the fermentation degree of black tea.
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•A self-built olfactory sensor including 1 pH and 3 TPP were developed.•Hyperspectral imaging technology was used to obtain sensory information of samples.•Different data fusion strategies were used to evaluate fermentation degree of black tea.
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches ...for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms’ development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
Structured Data Fusion Sorber, Laurent; Van Barel, Marc; De Lathauwer, Lieven
IEEE journal of selected topics in signal processing,
2015-June, 2015-6-00, Volume:
9, Issue:
4
Journal Article
Peer reviewed
Open access
We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, ...or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab's DSL.
Abstract
The openness and complexity of wireless channels make collaborative spectrum sensing vulnerable to malicious users1. Therefore, it is very important to identify the attributes of malicious ...users before collaborative spectrum awareness networks make data fusion decisions. In this paper, a method combining reinforcement learning and cognitive user credit model is proposed, in which the maximum and minimum eigenvalues of signals are used as the initial information for exchange, and the whole sensing network tends to diffuse and fuse nodes with high credit. Finally, the convergence value of the whole network is compared with the decision threshold to complete collaborative spectrum sensing. By combining with consensus fusion network and traditional collaborative sensing algorithm, the proposed method can effectively improve the convergence speed of fusion network and shorten the sensing time on the premise of effectively identifying malicious users2, so as to improve the spectrum sensing performance and make the collaborative sensing network more adaptive and stable.
Distributed Data Fusion (DDF) methods which possess guaranteed performance for ad-hoc and arbitrarily connected networks empower more scalable, flexible and robust information fusion for multi-robot ...sensor networks. This paper proposes a novel distributed Bayesian data fusion algorithm, which ensures uniform consistency, i.e., all the locally estimated distributions converge to the true distribution, for arbitrary periodically connected communication graphs. Conservative fusion via the Weighted Exponential Product (WEP) rule is utilized to combat inconsistencies that arise from double-counting common information between fusion agents, and the WEP fusion weight is chosen based on the dynamic communication network topology. The uniform consistency of the proposed algorithm is rigorously proved, and the cooperative consistency conditions that guarantee uniform consistency have been explicitly identified. The performance and convergence properties of the proposed algorithm are validated through simulations.
Deep convolutional neural networks have been applied by automobile industries, Internet giants, and academic institutes to boost autonomous driving technologies; while progress has been witnessed in ...environmental perception tasks, such as object detection and driver state recognition, the scene-centric understanding and identification still remain a virgin land. This mainly encompasses two key issues: 1) the lack of shared large datasets with comprehensively annotated road scene information and 2) the difficulty to find effective ways to train networks concerning the bias of category samples, image resolutions, scene dynamics, and capturing conditions. In this paper, we make two contributions: 1) we introduce a large-scale dataset with over 110 k images, dubbed DrivingScene, covering traffic scenarios under different weather conditions, road structures, and environmental instances and driving places, which is the first large-scale dataset for multi-class traffic scenes classification and 2) we propose a multi-label neural network for road scene recognition, which incorporates both single- and multi-class classification modes into a multi-level cost function for training with imbalanced categories and utilizes a deep data integration strategy to improve the classification ability on hard samples. The experimental results on DrivingScene and PASCAL VOC demonstrate the effectiveness of the proposed approach in handling the challenge of data imbalance.
This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The ...2018 Contest addressed the problem of urban observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspectral imaging, and very high-resolution imagery). The competition was based on urban land use and land cover classification, aiming to distinguish between very diverse and detailed classes of urban objects, materials, and vegetation. Besides data fusion, it also quantified the respective assets of the novel sensors used to collect the data. Participants proposed elaborate approaches rooted in remote-sensing, and also in machine learning and computer vision, to make the most of the available data. Winning approaches combine convolutional neural networks with subtle earth-observation data scientist expertise.
Deep learning -based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple data sources. ...However, due to privacy and confidentiality concerns, organisations often are unwilling or allowed to share their sensitive data with any external parties, thus making it challenging to build/train deep learning models for record linkage across different organisations' databases. To overcome this limitation, we propose the first deep learning-based multi-party privacy-preserving record linkage (PPRL) protocol that can be used to link sensitive databases held by multiple different organisations. In our approach, each database owner first trains a local deep learning model, which is then uploaded to a secure environment and securely aggregated to create a global model. The global model is then used by a linkage unit to distinguish unlabelled record pairs as matches and non-matches. We utilise differential privacy to achieve provable privacy protection against re-identification attacks. We evaluate the linkage quality and scalability of our approach using several large real-world databases, showing that it can achieve high linkage quality while providing sufficient privacy protection against existing attacks.
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