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In this paper, the problem of robust sliding mode control is studied for a class of discrete delayed nonlinear systems subject to randomly varying nonlinearities (RVNs) under uncertain occurrence probability. Here, the time-varying delay is bounded with known upper and lower bounds. The RVNs under uncertain occurrence probability are characterized by utilizing a Bernoulli distributed random variable,...
This paper is devoted to the consensus problem of fractional order multi-agent networks. A LMI condition for H∞ control of the fractional order uncertain systems is obtained. Based on this LMI condition, the H∞ consensus condition for fractional order multi-agent network is deduced by using graph theory and robust H∞ control theory. Finally, by simulations, the effectiveness of the LMI condition and...
Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation...
Deep learning has achieved great success in face recognition, however deep-learned features still have limited invariance to strong intra-personal variations such as large pose changes. It is observed that some facial attributes (e.g. eyebrow thickness, gender) are robust to such variations. We present the first work to systematically explore how the fusion of face recognition features (FRF) and facial...
Dictionary learning and component analysis are part of one of the most well-studied and active research fields, at the intersection of signal and image processing, computer vision, and statistical machine learning. In dictionary learning, the current methods of choice are arguably K-SVD and its variants, which learn a dictionary (i.e., a decomposition) for sparse coding via Singular Value Decomposition...
Recently, the fire-rescue ladder is towards to the direction of faster operation, lighter, extended length and larger outreach. The lightweight of the fire-rescue ladder and the limited stiffness of the ladder structure directly lead to different bending vibration problems in different modes. In this paper, the fire-rescue ladder is modeled as segmented Euler-Bemoulli beam with gravity and tip mass...
We propose a robust hand pose estimation method by learning hand articulations from depth features and auxiliary modality features. As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion. The proposed heat distribution descriptor is robust to identify the keypoints on the surface as it incorporates both the local...
In this paper we present a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the photometric information in the color sequence. Instead of making any assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. We are effectively calculating photometric stereo from a...
The power of modern image matching approaches is still fundamentally limited by the abrupt scale changes in images. In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views. Drawing inspiration from the scale space theory, we start with encoding the image’s scale space into a compact multi-scale representation. Then, rather than trying...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the kernel estimation process can significantly reduce the resulting image quality...
In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative...
Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and...
This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear...
We propose a robust method for estimating the orientation and displacement of an inertial measurement unit undergoing planar periodic motion. Such movements is a common approximation to human gait and running. We formulate the problem introducing a sparse vector of outlier errors and l1-regularization. The problem thus becomes robust to outliers in the data. The problem can be rewritten as a quadratic...
In this paper, we implemented a new McEliece encryption scheme, which consists of a optimal cyclic code to correct errors of burst. Replaces the matrix permutation by a random interleavers. In place of the vector of random error is used a vector of error that also helps to detect and correct errors of slip. This new scheme improves the confiability of the system and on the other hand increases the...
This paper presents a lambda tuner tool for the analysis and the design proportional-integral-derivative (PID) controllers using Matlab/Simulink software. The proposed application allows the user to easily obtain control parameters with lambda method and several traditional tuning methods. The lambda method guarantees robustness, stability and non-oscillatory response. It should be noted that the...
Teleconference systems are widely used in business and education. They are required to enhance only the target speech while suppressing diffuse or interference noise. The minimum variance beamformer is a strong method to enhance the target speech; However, it is less robust at low frequencies. We propose a robust beamformer based on the minimum variance method in the spherical harmonic domain. Less...
The article presents an intelligent traffic light system that allows optimal flow in a car crossing composed of two one-way streets. To achieve this purpose, real-time image processing is used to obtain the counting and space occupied by the vehicles. The algorithms of Backgroud Subtraction and KNN (K-Nearest Neighbor) are used, to determine the congestion vehicular in a street; the libraries of OpenCV...
In this paper, an optimal retail market pricing design for demand response in day-ahead scheduling of smart distribution networks is investigated. Through the well-designed retail market electricity price, the profit of each user is maximized; while the profit of the Distribution Network Operator (DNO) is guaranteed and the risk management model based on the Information Gap Decision Theory (IGDT)...
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