On Berry--Esseen bounds for non-instantaneous filters of linear processes

Let $X_n=\sum_{i=1}^{\infty}a_i\epsilon_{n-i}$, where the $\epsilon_i$ are i.i.d. with mean 0 and at least finite second moment, and the $a_i$ are assumed to satisfy $|a_i|=O(i^{-\beta})$ with $\beta >1/2$. When $1/2<\beta<1$, $X_n$ is usually called a long-range dependent or long-memory process. For a certain class of Borel functions $K(x_1,...,x_{d+1})$, $d\ge0$, from ${\mathcal{R}}^{d+1}$ to $\mathcal{R}$, which includes indicator functions and polynomials, the stationary sequence $K(X_n,X_{n+1},...,X_{n+d})$ is considered. By developing a finite orthogonal expansion of $K(X_n,...,X_{n+d})$, the Berry--Esseen type bounds for the normalized sum $Q_N/\sqrt{N},Q_N=\sum_{n=1}^N(K(X_ n,...,X_{n+d})-\mathrm{E}K(X_n,...,X_{n+d}))$ are obtained when $Q_N/\sqrt{N}$ obeys the central limit theorem with positive limiting variance.Comment: Published in at http://dx.doi.org/10.3150/07-BEJ112 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring

Image motion blur usually results from moving objects or camera shakes. Such blur is generally directional and non-uniform. Previous research efforts attempt to solve non-uniform blur by using self-recurrent multi-scale or multi-patch architectures accompanying with self-attention. However, using self-recurrent frameworks typically leads to a longer inference time, while inter-pixel or inter-channel self-attention may cause excessive memory usage. This paper proposes blur-aware attention networks (BANet) that accomplish accurate and efficient deblurring via a single forward pass. Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different degrees and with cascaded parallel dilated convolution to aggregate multi-scale content features. Extensive experimental results on the GoPro and HIDE benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-art in blurred image restoration and can provide deblurred results in real-time

Modeling of Location Estimation for Object Tracking in WSN

Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting value α, and power-level increasing ratio k of neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications

An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm

This paper proposes a research of An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm and combines 3D graphic application interfaces, such as DirectX3D and OpenCV to reconstruct the 3D imaging system for Magnetic Resonance Imaging (MRI), and adds Level of Detail (LOD) algorithm to the system. The system uses the volume rendering method to perform 3D reconstruction for brain imaging. The process, which is based on the level of detail algorithm that converts and formulates functions from differing levels of detail and scope, significantly reduces the complexity of required processing and computation, under the premises of maintaining drawing quality. To validate the system's efficiency enhancement on brain imaging reconstruction, this study operates the system on various computer platforms, and uses multiple sets of data to perform rendering and 3D object imaging reconstruction, the results of which are then verified and compared

Differential Gene Expression in Response to Papayaringspot virus Infection in Cucumis metuliferus UsingcDNA- Amplified Fragment Length PolymorphismAnalysis

A better understanding of virus resistance mechanisms can offer more effective strategies to control virus diseases. Papayaringspot virus (PRSV), Potyviridae, causes severe economical losses in papaya and cucurbit production worldwide. However,no resistance gene against PRSV has been identified to date. This study aimed to identify candidate PRSV resistance genesusing cDNA-AFLP analysis and offered an open architecture and transcriptomic method to study those transcriptsdifferentially expressed after virus inoculation. The whole genome expression profile of Cucumis metuliferus inoculated withPRSV was generated using cDNA-amplified fragment length polymorphism (cDNA-AFLP) method. Transcript derivedfragments (TDFs) identified from the resistant line PI 292190 may represent genes involved in the mechanism of PRSVresistance. C. metuliferus susceptible Acc. 2459 and resistant PI 292190 lines were inoculated with PRSV and subsequentlytotal RNA was isolated for cDNA-AFLP analysis. More than 400 TDFs were expressed specifically in resistant line PI 292190. Atotal of 116 TDFs were cloned and their expression patterns and putative functions in the PRSV-resistance mechanism werefurther characterized. Subsequently, 28 out of 116 candidates which showed two-fold higher expression levels in resistant PI292190 than those in susceptible Acc. 2459 after virus inoculation were selected from the reverse northern blot andbioinformatic analysis. Furthermore, the time point expression profiles of these candidates by northern blot analysissuggested that they might play roles in resistance against PRSV and could potentially provide valuable information forcontrolling PRSV disease in the future