Description Of Hierarchical Structure Research Articles

A Lightweight Convolutional Neural Network for Hyperspectral Image Classification

In the hyperspectral image, each pixel corresponds to a small area on the Earth's surface and represents the intrinsic characteristic of objects, which can be applied for recognition of land covers. Nevertheless, hyperspectral image processing should face some critical issues, and a small sample set problem may be the most challenging one in the research. Deep learning (DL), which has successfully been applied in many fields, has also been introduced for hyperspectral image classification. However, the large gap between the massive parameters to be tuned and limited labeled samples can lead to overfitting scenario, inevitably deteriorating the generalization ability of the DL model. In this article, a lightweight convolutional neural network (LWCNN) is proposed for hyperspectral image classification to mainly tackle the small sample set problem. Especially, spatial-spectral Schroedinger eigenmaps (SSSE) feature extraction is first adopted to obtain the joint spatial-spectral information, and the compressed dimensionality could significantly reduce the number of parameters in the following DL model. Second, a dual-scale convolution (DSC) module is carefully designed to address the SSSE features from a 1-D vector viewpoint (the number of parameters is further decreased), and the DSC procedure is successively employed to obtain the hierarchical structure description that could represent data distribution from different aspects. Subsequently, the feature vectors from all DSC layers are separately filtered by a new bichannel fusion (BCF) module, which could well encode both the intrinsic and contextual information inside DSC features. Finally, the filtered features are concatenated together and imported into a global average pooling classifier to achieve the predicted probability of each category. Experimental results on three famous hyperspectral image data sets illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples.

Early differential sensitivity of evoked-potentials to local and global shape during the perception of three-dimensional objects

Here we investigated the time course underlying differential processing of local and global shape information during the perception of complex three-dimensional (3D) objects. Observers made shape matching judgments about pairs of sequentially presented multi­part novel objects. Event-related potentials (ERPs) were used to measure perceptual sensitivity to 3D shape differences in terms of local part structure and global shape configuration – based on predictions derived from hierarchical structural description models of object recognition. There were three types of different object trials in which stimulus pairs (1) shared local parts but differed in global shape configuration; (2) contained different local parts but shared global configuration or (3) shared neither local parts nor global configuration. Analyses of the ERP data showed differential amplitude modulation as a function of shape similarity as early as the N1 component between 146–215ms post-stimulus onset. These negative amplitude deflections were more similar between objects sharing global shape configuration than local part structure. Differentiation among all stimulus types was reflected in N2 amplitude modulations between 276–330ms. sLORETA inverse solutions showed stronger involvement of left occipitotemporal areas during the N1 for object discrimination weighted towards local part structure. The results suggest that the perception of 3D object shape involves parallel processing of information at local and global scales. This processing is characterised by relatively slow derivation of ‘fine-grained’ local shape structure, and fast derivation of ‘coarse-grained’ global shape configuration. We propose that the rapid early derivation of global shape attributes underlies the observed patterns of N1 amplitude modulations.

Fractal theoretic aspects of local field

Recently, some approaches to fractal sets are taken based on theory of function spaces. One of them is the study of stochastic processes by introducing Dirichlet space on self-similar structure. In other studies, description of hierarchical structure by the shift space gives a clear structural grip of self-similarities. An important subject in this direction is a construction of uniformly distributed sequence based on the shift space. In this article, we will reveal a relationship with probabilistic significance between Besov space and potential space by showing trace theorem and the property of sequence of non-negative integers as uniformly distributed sequence.

Hierarchical structure description of intermittent structures of turbulence

It is shown that intermittent structures in turbulence can be quantitatively described in the framework of the hierarchical structure model of She and Leveque (Phy. Rev. Lett. 72 (1994) 366). Nearly isotropic turbulence can be characterized by a universal hierarchical symmetry parameter β and a characteristic (non-universal) singularity index γ. It is demonstrated that both β and γ can be directly obtained from an analysis of experimental velocity data in terms of the velocity structure functions and their scaling exponents. The procedure is validated by the analysis of a series of turbulence data including a swirling turbulent flow, a 3-D DNS turbulence data, and a GOY shell model of turbulence. The physical interpretation of the parameters β and γ suggests that this quantification provides a physically meaningful characterization of different turbulent environments. A convergence study of moments and scaling exponents is carried out with a detailed analysis of finite sample size effects.

Design of hierarchical cellular automata for on-chip test pattern generator

This paper introduces the concept of hierarchical cellular automata (HCA). The theory of HCA is developed over the Galois extension field GF(2(pqr..)), where each cell of the CA can store and process a symbol in the extension field GF (2(pqr..)). The hierarchical field structure of GF(2(pqr..)) is employed for design of an HCA-based test pattern generator (HCATPG). The HCATPG is ideally suited for testing very large scale integration circuits specified in hierarchical structural description. Experimental results establish the fact that the HCATPG achieves higher fault coverage than that which could be achieved with any other test structures. The concept of percentile improvement in fault coverage is introduced to have a realistic assessment of fault coverage achieved with the proposed RCATPG.

Real Time Quality Optimization in Flexible Manufacturing Systems

In this paper a quality oriented model is proposed for product and process as a whole. A hierarchical structural description allows both model consistency and integration of detailled information. Material and data flows are so perfectly defined. Inaccurate or uncertain data are modelled by mean of fuzzy quantities. Associated operators and relations permit information propagation in the model. That strict but flexible approach shows actual advantages above standard statistical methods. Product quality and process operation are valued in order to build a goal cost function. That criterion is used to define the optimal control strategy. A distributed computation process reduces information exchanges and contributes greatly to a safe on-line quality management.

Flute an Expert Floor Planner for Full-Custom VLSI Design

Flute is a heuristic floorplanner that operates as part of Cadre, a system of cooperating expert agents for converting a hierarchical structural description into full-custom VLSI layout. Flute is modeled on the human floorplanning process and uses a mixture of rule-based programming, state control, and algorithmic operators. Initially, a topological plan is generated by placing modules on a grid graph. From this plan, a constraint graph is prepared. This graph is solved to add geometric size and placement information to the floorplan. Though Flute handles a fairly small number of submodules at a time, it can construct a floorplan of complex VLSI layout using a hierarchical structure of ICs. Flute is implemented in Zeta-lisp on Symbolics Lisp machines.