- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detecting neural assemblies in calcium imaging data
摘要： Background: Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown. Results: To test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses. Conclusions: Our findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.
关键词： Population coding,Clustering,Spontaneous activity
更新于2025-09-23 15:21:01
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Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images
摘要： Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve re?ned detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship ?eet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware.
关键词： ship detection,optical remote sensing,saliency,scene partition,feature clustering
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - St. Petersburg and Moscow, Russia (2020.1.27-2020.1.30)] 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - Study of External Flashes Influence and False Operation on Work Stability of Laser Speed Sensors
摘要： Relational fuzzy clustering (RFC) algorithms prove very useful in Web user session clustering because Web user sessions may contain fuzzy, conflicting and imprecise information. Though RFC algorithms are very sensitive to cluster initialization and works only if the numbers of clusters are specified in advance. However, at all times, the prior initialization of a number of clusters is not feasible due to the dynamically evolving nature of user sessions. Therefore, estimating the number of clusters and initializing suitable cluster prototype are a significant performance bottleneck in this method. In this paper, the discounted fuzzy relational clustering (DFRC) algorithm is proposed to address the major constraint of RFC. The DFRC algorithm identifies Web user session clusters from Web server access logs, without initializing the number of clusters and prototypes of initial clusters. The DFRC algorithm works in two stages. In the first stage, DFRC automatically identifies the number of potential clusters based on the successively discounted potential density function value of each relational data and their respective centres. In the second stage, DFRC assigns fuzzy membership values to each data point and forms fuzzy clusters from the relational matrix. The DFRC algorithm is applied on an augmented session dissimilarity matrix obtained from a publicly accessed NASA Web server log data. The experimental results are evaluated using different fuzzy validity measures. The extensive experiments are performed to test the effect of various parameters, including accept/reject ratio and neighbourhood radius on the performance of DFRC algorithm. The results were also compared with fuzzy relational clustering algorithm using cluster quality measures. It is observed that the quality of generated clusters using DFRC is superior as compared with that of RFC.
关键词： relational fuzzy clustering,fuzzy validity index,cluster quality,similarity measures,subtractive clustering,Augmented sessions
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Perovskite solar cell devices on flexible stainless-steel substrate
摘要： Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types.
关键词： fuzzy ART,mixed-type data,unsupervised feature learning,Clustering
更新于2025-09-23 15:19:57
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Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
摘要： The contribution and impact of combined laser ablation inductively coupled plasma time of flight mass spectrometry (LA-ICP-TOF-MS) and laser induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This Tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc), and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace elements emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and the combined data of TOF and LIBS, respectively, as the input for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal classification performance, with a classification accuracy of up to 98%.
关键词： Principal component analysis,Support vector machine,Partial least squares discrimination analysis,Laser-induced breakdown spectroscopy,K-means clustering,Coal discrimination,Laser ablation inductively coupled plasma time of flight mass spectrometry
更新于2025-09-23 15:19:57
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[IEEE 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Hurgada, Egypt (2019.11.17-2019.11.20)] 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Co-Planar Waveguide Resonator to Mediate Coupling between Superconducting Quantum Bits
摘要： Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
关键词： security,multi-keyword search,Cloud computing,ranked search,hierarchical clustering,ciphertext search
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Atomic Layer Deposited Al <sub/>x</sub> Ni <sub/>y</sub> O as Hole Selective Contact for Silicon Solar Cells
摘要： Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.
关键词： vector quantization,Approximate nearest neighbor search,subspace clustering,large-scale retrieval,binary codes
更新于2025-09-23 15:19:57
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Photocurrent improvement of an ultra-thin silicon solar cell using the localized surface plasmonic effect of clustering nanoparticles
摘要： The cluster-shaped plasmonic nanostructures are used to manage the incident light inside an ultra-thin silicon solar cell. Here, for the first time, spherical, conical, pyramidal, and cylindrical nanoparticles as a form of a cluster in the rear side of a thin silicon cell are simulated using finite difference time domain (FDTD) method. By calculating the optical absorption and hence the photocurrent, it is shown that the clustering of nanoparticles significantly improves them. The photocurrent enhancement is the result of the plasmonic effects of clustering the nanoparticles. For more comparison, at first, a cell with a single nanoparticle at the rear side is evaluated. Then four smaller nanoparticles are put around it to make a cluster. The photocurrents of 20.478, 23.186, 21.427, and 21.243 mA/cm2 are obtained for the cells using clustering conical, spherical, pyramidal, cylindrical NPs at the backside, respectively. These values are 13.987, 16.901, 16.507, 17.926 mA/cm2 for the cell with one conical, spherical, pyramidal, cylindrical nanoparticle at the rear side, respectively. So, clustering significantly improves the photocurrents. Finally, the distribution of the electric field and the generation rate for the proposed structures are calculated.
关键词： Clustering NPs,Localized surface plasmon resonance,light management,FDTD,Photocurrent,Plasmonic solar cell
更新于2025-09-23 15:19:57
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Uniform Star Catalogue using GWKM Clustering for Application in Star Sensors
摘要： In this paper, a novel algorithm of weighted k-means clustering with geodesic criteria is presented to generate a uniform database for a star sensor. For this purpose, selecting the appropriate star catalogue and desirable minimum magnitude and eliminating double stars are among the steps of the uniformity process. Further, Delaunay triangulation and determining the scattered data density by using a Voronoi diagram were used to solve the problems of the proposed clustering method. Thus, by running a Monte Carlo simulation to count the number of stars observed in different fields of view, it was found that the uniformity leads to a significant reduction of the probability of observing a large number of stars in all fields of view. In contrast, the uniformity slightly increased the field of view needed to observe the minimum number of required stars for an identification algorithm.
关键词： Geodesic k-means clustering,Scattered data density,Delaunay triangulation,Optimized star catalogue
更新于2025-09-19 17:15:36
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[IEEE 2018 7th European Workshop on Visual Information Processing (EUVIP) - Tampere, Finland (2018.11.26-2018.11.28)] 2018 7th European Workshop on Visual Information Processing (EUVIP) - Automatic 3D Detection and Segmentation of Head and Neck Cancer from MRI Data
摘要： A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MRI volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.
关键词： fuzzy clustering,magnetic resonance imaging,Fourier interpolation,head and neck cancer,3D level set method
更新于2025-09-19 17:15:36