Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs

The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain–computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically. This paper presents a novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial–temporal patterns from the EEG signals and the corresponding neuronal activities. The proposed method comprises four progressive stages: multi-time segment and temporal frequency band-pass filtering, CSP spatial filtering, mutual information-based feature selection and naïve Bayesian classification. The proposed mutual information-based selection of optimal spatial–temporal patterns and its one-versus-rest multi-class extension were evaluated on single-trial EEG from the BCI Competition IV Datasets IIb and IIa respectively. The results showed that the proposed method yielded relatively better session-to-session classification results compared against the best submission.     

Landsat-satellite-based analysis of spatial–temporal dynamics and drivers of CyanoHABs in the plateau Lake Dianchi

ABSTRACT

Dianchi Lake, located in southwest China’s Yungui plateau, is facing severe eutrophication and frequent outbreaks of harmful cyanobacteria blooms (CyanoHABs). It is of great significance to monitor the occurrence and development of CyanoHABs in Dianchi Lake over a long period and analyse the main influences. Based on Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager 1986–2016 data, we derived the distribution of the CyanoHABs in Dianchi Lake, and analysed spatial–temporal dynamics of the CyanoHABs by correlation with nutrition, meteorological, and humanities data. The results showed that the first outbreak of CyanoHABs in Dianchi Lake occurred in 1987, which is likely to be influenced by a rapid increase of nutrients in the lake, while the weather conditions also have some impact on the CyanoHABs occurrence. After 1990, the frequency of CyanoHABs is relatively high in the water near Longmen village, Fubao Bay, Hui Bay, and the lake inlet of the Panlong River to the north of Waihai in Dianchi Lake from June to November every year. Moreover, the CyanoHABs increased year by year until 2000. This is closely related to population growth and economic development. Furthermore, a large amount of precipitation and small wind speeds can also promote the occurrence of CyanoHABs. After 2000, the frequency of CyanoHABs decreased, as the large-scale management of water pollution in Dianchi Lake achieved certain effects. The area and frequency of CyanoHABs from 2011 to 2014 are the smallest in the last 20 years, which may be related to the large-scale planting of Eichhornia crassipes in the north of Dianchi Lake.     

Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data

Traffic flow prediction is a fundamental component in intelligent transportation systems. Various computational methods have been applied in this field, among which machine learning based methods are believed to be promising and scalable for big data. In general, most of machine learning based methods encounter three fundamental issues: feature representation of traffic patterns, learning from single location or network, and data quality. In order to address these three issues, in this work we present a deep architecture for traffic flow prediction that learns deep hierarchical feature representation with spatio-temporal relations over the traffic network. Furthermore, we design an ensemble learning strategy via random subspace learning to make the model be able to tolerate incomplete data. Accordingly the contributions of this work are summarized as the three points. First, we transform the time series analysis problem into the task of image-like analysis. Benefitting from the image-like data form, we can jointly explore spatio-temporal relations simultaneously by the two-dimension convolution operator. In addition, the proposed model can tolerate the incomplete data, which is very common in traffic application field. Finally, we propose an improved random search based on uniform design in order to optimize hyper-parameters for deep Convolutional Neural Networks (deep CNN). A large range of experiments with various traffic conditions have been performed on the traffic data originated from the California Freeway Performance Measurement System (PeMS). The experimental results corroborate the effectiveness of the proposed approach compared with the state of the art.     

Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognition

Pattern Analysis and Applications - The skeleton data convey significant information for action recognition since they can robustly against cluttered backgrounds and illumination variation. In...     

Multi‐temporal analysis of MODIS data to classify sugarcane crop

This paper presents a feasibility study using multi‐temporal Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data to classify sugarcane crop. This study was carried out in São Paulo State which is the major sugarcane producer in Brazil, occupying more than 3.1 million hectares. Cloud‐free MODIS images (16 days mosaics) were acquired over a period of almost 15 months. Samples of sugarcane and non‐sugarcane were randomly selected and cluster analysis was performed to establish similar EVI temporal behaviour clusters. It was observed that EVI was sensitive to variations in land‐use cover mainly due to phenology and land management practices. Therefore, selection of sugarcane samples with similar EVI temporal behaviour for supervised classification was difficult due to both large planting and large harvesting periods. Consequently, cluster analysis was chosen to carry out an unsupervised classification. The best results were obtained in regions occupied by: natural and planted forest, soybean, peanuts, water bodies and urban areas which contrasted with the temporal‐spectral behaviour of sugarcane. The lowest performance was observed mainly in regions dominated by pasture, which has similar temporal‐spectral behaviour to sugarcane. This study provided useful information to define a MODIS image classification procedure for sugarcane crop for the whole State area based on the large amount of cloud‐free MODIS images when compared with other currently available optical sensors.     

A spatial–temporal analysis of impacts from human development on the Shih-men Reservoir watershed,Taiwan

Human activity such as the development of slope land around watersheds has dramatically affected the ecological environment in Taiwan. This situation has been aggravated by heavy precipitation from typhoons in the summertime. The results include serious soil erosion and mass movement in the Shih-men Reservoir watershed. In order to identify the most fragile areas and seek the triggering factors of landslide changes that can cause turbid currents in the Shih-men Reservoir watershed, this study integrates different types of satellite imagery and geographic information system data to determine changes in land cover and vegetation cover since the early 1970s. Results from spatial regression models indicate road and land uses are the main factors that lead to slope failure along roads and contribute to a large number of landslides in environmental hotspots like the Baishih River sub-watershed. Soil erosion estimates indicate a positive relationship between the increases in landslide and soil loss areas and the road system development. Therefore, human development has a significant negative influence both on sensitive mountainous watersheds and on critical environmental hotspots.     

Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8–838.7 nm) were collected from leaves of infested and non‐infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non‐infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly‐induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress.     

A visualization framework for the analysis of?neuromuscular?simulations

We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion analysis. In studying these data using a 3D visualization approach, interactive exploring is important, especially for supporting spatial analysis. Moreover, as these data contain many various but related elements, numerical analysis of neuromuscular simulations is complicated. Visualization techniques enhance the analysis process, thus improving the effectiveness of the experiments. Our approach allows convenient definitions of relationships between numerical data sets and 3D objects. Scientific simulation data sets appropriate for this style of analysis are present everywhere motion analysis is performed and are predominant in many clinical works. In this paper, we outline the functionalities of the framework as well as applications embedded within the OpenSim simulation platform. These functionalities form an effective approach specifically designed for the investigation of neuromuscular simulations. This claim is supported by evaluation experiments where the framework was used to analyze gaits and crouch motions.     

Development of a novel system to quantify the spatial–temporal parameters for crutch-assisted quadrupedal gait

Abstract

Patients with gait disorders often use bilateral crutches along with their own two legs. It is a kind of quadrupedalism. Crutch-assisted gait is usually described and evaluated qualitatively. In this study, we developed a system to quantify the spatial and temporal parameters for crutch-assisted quadrupedalism. Our system consists of walkway hardware and our originally developed software. We specifically extended the measurable area to 1200 mm × 4800 mm, large enough to measure crutch gait. Using our system, we could describe crutch gait precisely. Our system has a capability to evaluate differences between patients and changes within a patient.     

STTG-TTE: spatial–temporal gated multi-modality approach for travel time estimation based on temporal convolutional networks

Neural Computing and Applications - Travel time forecasting has become a core component of smart transportation systems, which assists both travelers and traffic organizers with route planning,...     

A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things

The Journal of Supercomputing - Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy...     

Robust spatial–temporal Bayesian view synthesis for video stitching with occlusion handling

Occlusion is visible in only one frame and cannot be seen in the other frame which is a vital challenge in video stitching. Occlusion always brings ghost artifacts in the blended area. Meanwhile, the traditional image stitching approaches ignore temporal consistency and cannot avoid flicking problem. To solve these challenges, we propose a unified framework in which the stitching quality and stabilization both perform well. Specifically, we explicitly detect the potential occlusion regions to indicate blending information. Then, based on the occlusion maps, we choose a proper strip in the overlapped region as the blending area. With spatial–temporal Bayesian view synthesis, spatial ghost-like artifacts can be significantly eliminated and the output videos can be kept stable. The experimental results show the out performance of the proposed approach compared to state-of-the-art approaches.     

Online decentralized information gathering with spatial–temporal constraints

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.     

Using tuple-spaces to manage the storage and dissemination of spatial–temporal content

Structured, spatial–temporal content arises in application areas such as mobile computing, intelligent transportation, urban mobility, and ubiquitous sensing. For the distributed storage and dissemination of such content, peer-to-peer solutions appear to be the natural choice. However, a closer analysis shows that distributed hash tables (DHT) alone are not enough: firstly, they do not maintain the original data structure needed to efficiently access the comprising attributes, and secondly, they lead to high signaling traffic when the data is short lived, such as in high mobility scenarios. In order to address these two problems we propose a novel content dissemination architecture based on an overlay of space-based containers. Furthermore, we apply the proposed concept to realize a concrete application in the field of intelligent transportation, and present the results of the performance evaluation conducted with the system prototype.     

Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

The high dimensionality of hyperspectral images are usually coupled with limited data available, which degenerates the performances of clustering techniques based only on pixel spectral. To improve the performances of clustering, incorporation of spectral and spatial is needed. As an attempt in this direction, in this paper, we propose an unsupervised co-clustering framework to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated using an undirected bipartite graph. The optimal partitions are obtained by spectral clustering on the bipartite graph. Experiments on four hyperspectral data sets are performed to evaluate the effectiveness of the proposed framework. Results also show our method achieves similar or better performance when compared to the other clustering methods.     

Fast spatial–temporal stereo matching for 3D face reconstruction under speckle pattern projection

Three-dimensional (3D) face reconstruction can be tackled in either measurement-based means or model-based means. The former requires special hardwares or devices, such as structured light setups. This paper addresses 3D face reconstruction by measurement-based means, more specifically a special kind of structured light called space–time speckle projection. Under such a setup, we propose a novel and efficient spatial–temporal stereo scheme towards fast and accurate 3D face recovery. To improve the overall computational efficiency, our scheme consists of a series of optimization strategies including face-cropping-based stereo matching, coarse-to-fine stereo matching strategy applied to face areas, and spatial–temporal integral image (STII) for accelerating the matching cost computation. Based on the results, the proposed scheme is able to reconstruct a 3D face in hundreds of milliseconds on a normal PC, and its performance is validated both qualitatively and quantitatively.     

EPSILON: a versatile microcomputer program for the spectrophotometric data analysis of metal–ligand equilibria

We have developed the microcomputer program EPSILON which allows the computation of equilibrium constants and extinction coefficients of several interacting species from spectrophotometric single- and multi-wavelength pH-titration data. Novel features of EPSILON include: easy treatment of the data through a windowed environment, minimization of errors with respect to the read variable (absorbance), simultaneous plotting of both the spectrophotometric titration curve and the species distribution as a function of pH during refinement and estimation of standard deviations for the refined parameters. Several examples of applications are provided and discussed in order to illustrate the use of EPSILON. The results are compared with literature values and with those obtained by BEST from potentiometric data.     

A cloud computing framework for analysis of agricultural big data based on Dempster–Shafer theory

The Journal of Supercomputing - This paper aims to extract optimal location for cultivating orange trees. In order to reach this goal, a combination of Dempster-Shafer theory (DST) and cloud...     

The evolutionary trajectory of an ICT ecosystem: A network analysis based on media users’ data

We integrate users into the visualization and analysis of an information and communication technology (ICT) ecosystem by using demand-side data. We also broaden the ecosystem layer model by using a media repertoire concept and propose a clear method of showing evolutionary trends. Consequently, we discover the nature of an evolutionary path in an ICT ecosystem. This path becomes more rigid and centralized as it matures, a finding that agrees with prior studies’ results that used supply-side data. Further, we analyze the trends of firms changing layer positions and suggest multiple approaches of visualizing and analyzing interfirm relationships for practitioners.     

Forecasting traffic flow with spatial–temporal convolutional graph attention networks

Neural Computing and Applications - Traffic flow prediction is crucial for intelligent transportation system, such as traffic management, congestion alleviation and public risk assessment....