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Broadcasting (one-to-all communication) is one of most important and fundamental research problems for information dissemination in all types of networks. In this paper, we mainly focus on the minimum-latency broadcasting in known topology Wireless Mesh Networks (WMNs), in which the network topology and the size is known in advance. It is well known that the computation of a minimum-latency broadcasting...
Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop...
The paper compares filtered PID control with two filtered Smith predictor modifications and experimentally points out different impact of the derivative terms in the considered controllers on the loop robustness and performance. All structures applied to a thermal plant control are based on its approximation by the first order time-delayed model. They include an nth order binomial filter for measurement...
Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image...
Localization of a viewer's region of interest (ROI) on eye gaze signal trajectories acquired by eye trackers is a widely used approach in scene analysis, image compression, and quality of experience assessment. In this paper, we propose a novel clustering approach for ROI estimation from potentially noisy raw eye gaze data, based on signal processing on graphs. The clustering approach adapts graph...
In this paper, we propose a novel point set matching algorithm to improve the matching precision in the presence of non-Gaussian noises and outliers. In our method, a non-second order similarity measure known as Kernel Mean p-Power Error (KMPE) loss is employed as the matching cost function. We introduce a local optimal solution for computing the rigid transform by repeating the correspondence estimation...
Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While...
This paper reports on a frequency agile GaN LNA MMIC that can be reconfigured for both S- and X-band extending operation over multiple octaves of frequency. The LNA is based on a 0.15um GaN HEMT technology and utilizes GaN FET switches to tune the LNA for 3–3.5 GHz Sband and 9–11 GHz X-band operation. Switch tuned for Sband, the amplifier achieves 15 dB of gain, NF ranging from 0.9–1.3 dB, an input...
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications. Even though RPCA has been shown to be very successful in solving many rank minimisation problems, there are still cases where degenerate or suboptimal solutions are obtained. This...
One of the challenging aspects in directional electromagnetic measurements is to accurately estimate the formation bedding orientation angle, which is critical to help maintain drilling the wellbore in the sweetest spots of the reservoir. Various existing methods can be both noisy and susceptible to phase wrapping issues. Here we present a new approach for accurately computing the bedding orientation...
The work deals with researching robustness of information system for measuring of microcontrollers average power consumption. Defined the ways of ensuring robustness of the proposed method. Considered hardware for robustness of the proposed method and proposed methods of study of the robustness of the proposed method.
Shape from focus technique can be used in the computer monocular vision, which is widely applied in the smart transportation. In this study, we proposed a novel directional statistics based focus measure for shape from focus computation. We first compute the standard deviation σ and the mean value μ in the directional neighborhood. Then use the σ/μ as the focus measure to estimate the shape. The proposed...
This paper considers the problem of range-based decentralized localization in wireless sensor networks when the impulsive measurement noise is present. We develop a robust localization estimator requiring no a priori knowledge of the noise distribution. The approach to robust localization presented here follows the concept of M-estimation and is implemented in a decentralized manner thus suiting the...
Both state propagation and sensor measurements are often corrupted by unmodeled non-Gaussian or heavy-tailed noise. Without dealing with such outliers, the accuracy of a estimator significantly degrades, and control systems that rely on high-quality estimation lose stability. To estimate the states of dynamic systems in which both types of outliers occur, we propose a novel approach that combines...
This paper addresses the problem of sequential binary hypothesis testing in a multi-agent network to detect a random signal in non-Gaussian noise. To this end, the con-sensus+innovations sequential probability ratio test (ciSPRT) is generalized for arbitrary binary hypothesis tests and a robust version is developed. Simulations are performed to validate the performance of the proposed algorithms in...
Background Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on...
In this work, we investigate robust speech energy estimation and tracking schemes aiming at improved energy-based multiband speech demodulation and feature extraction for multi-microphone distant speech recognition. Based on the spatial diversity of the speech and noise recordings of a multi-microphone setup, the proposed Multichannel, Multiband Demodulation (MMD) scheme includes: 1) energy selection...
Closed Curve approximation is a technique to approximate a digital planar curve with piece straight line segments. The terminating point of a candidate line segment is known as pseudo point. By detecting good choice of the pseudo point on the digital planar one may be able to visibly recognize the shape of the curve. The techniques analyzed in this paper makes closed curve approximation by deleting...
The paper proposed a robust optimum thresholding method based on local intensity mapping(LIM), class uncertainty and region stability theories to segment fuzzy and noisy images. First of all, the intensities of an image would be mapped into another intensity space by LIM which could decrease the influence of noise and uneven intensity distribution. Then, the intensity-based class uncertainty is applied...
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatially harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the...
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