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Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years...
Optical incremental encoder is extensively used in motion control to obtain position or/and velocity information. The calculation of velocity from finite discrete position pulses inherently will produce lots of noise that seriously affects the performance of servo derive system. Based on the analysis of mechanism of velocity measurement, a novel acceleration estimation algorithm is proposed by combining...
In iris recognition systems, iris localization is a critical step which affects the further results definitively. Most of the traditional localization methods were time consuming and sensitive to noises. To solve the problems, we propose an algorithm which adopts a momentum based level set method to locate the pupil boundary. This method hasan advantage of decreasing the effect of local optima solutions...
In the recent years, contour-based shape representation is an important issue in the object recognition research area. In this paper, a new shape descriptor A-DCE is proposed based on DCE and DP for contour deformation and recognition. Firstly, the object contour is evolved adaptively by DCE to extract the contour information with important visual parts. Secondly, the costing feature descriptor is...
When the system model and noise statistical characteristics are known, the conventional Kalman filtering algorithm is suitable. In most cases, the noise statistics are unknown. To improve the alignment precision and convergence speed of strap-down inertial navigation system, an initial alignment method based on Sage-Husa adaptive filter is proposed. Automatic on-line estimation and correction for...
Human listeners are capable of recognizing speech in noisy environment, while most of the traditional speech recognition methods do not perform well in the presence of noise. Unlike traditional Mel-frequency cepstral coefficient (MFCC)-based method, this study proposes a phoneme classification technique using the neural responses of a physiologically-based computational model of the auditory periphery...
Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small...
Almost every computer vision applications used background subtraction method to detect moving objects from video sequence. Moving object detection and tracking is generally the first step in many applications such as face detection, traffic surveillance, object recognition, detection of unattended bags, people counting etc. Background modeling is very useful and effective method for locating objects...
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant...
Most of the studies working on point cloud data focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. We study noise handling, and we put our focus on processing a noisy point cloud of a visual object or a 3D model. We propose...
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental...
In cognitive radio networks, spectrum sensing plays a crucial role in the discovery of spectrum opportunities for secondary systems (or unlicensed systems). The performance of spectrum sensing is characterized by both accuracy and efficiency, and more importantly the time taken to make a decision and also the complexity involved in doing so. In this work we propose a simple detection technique based...
Protocols such as CEI, 10G Ethernet and PCIe Gen3 are requiring very long pattern length stress signals such as PRBS-23 and PRBS-31 to claim compliance and ensure robustness. Unfortunately, there is equipment limitation to directly measure very long pattern signals especially on oscilloscopes due to memory size and signal processing power. Thus, new jitter decomposition algorithms are introduced to...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
In this paper, joint sensor synchronization and localization using time-of-arrival measurements is studied. In wireless sensor networks, the accuracy of the clock synchronization among nodes has a great impact on the performance of the localization using time-based ranging methods. The clocks of the anchor nodes are typically synchronized with each other, while those of the source nodes must be synchronized...
We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes;...
We analyse accuracy, privacy, compressionratio and computational overhead of selected aggregation and perturbation methods in the Internet of Things (IoT). We measure over a real-life data set of detailed energy consumption logs of a single family household. We modelled privacy by simple, threshold-driven machine-learning algorithms that extract features of behaviour. The accuracy of those extraction...
Higher order statistics based subspace methods are extensively used for Direction of Arrival (DOA) estimation. This paper compares different versions of fourth order cumulant based ESPRIT algorithms for DOA estimation in terms of accuracy, resolution and computational complexity. The popularity of cumulant based methods is because of their better resolution and their ability to perform well even in...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
CAPTCHAs exploit the gap in the ability between a human and a machine to understand the semantics of specific multimedia content, with vast applications in computer security. In this paper we compare two techniques in automated CAPTCHA solving for text-based CAPTCHA schemes, i.e., Classification based on the Vector Space Model (VSM) versus a popular Optical Character Recognition (OCR) engine. For...
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