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How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with...
An interesting observation about the well-known AdaBoost algorithm is that, though theory suggests it should overfit when applied to noisy data, experiments indicate it often does not do so in practice. In this paper, we study the behavior of AdaBoost on datasets with one-sided uniform class noise using linear classifiers as the base learner. We show analytically that, under some ideal conditions,...
The paper considers the problem of feature selection in learning using privileged information (LUPI), where some of the features (referred to as privileged ones) are only available for training, while being absent for test data. In the latest implementation of LUPI, these privileged features are approximated using regressions constructed on standard data features, but this approach could lead to polluting...
In recent years, the introduction of solar power generation equipment has increased as distributed power supply has increased. A power conditioning subsystem (PCS) of a solar power generation system is an apparatus using an integrated circuit. Switching noise is generated because the inverter, which is the main constituent element of the PCS, performs DC-AC conversion through high-frequency switching...
Method noise which is the difference between a noisy image and its denoised version, often contains image structure and detail information due to imperfect denoising. This paper analyzes the method noise and establishes a model to extract image information submerged in the method noise. Then the extracted image information is fed back to the denoised image to conduct the next denoising step. The whole...
Line impedance stabilization network (LISN) is an important tool to provide noiseless power supply in electromagnetic compatibility (EMC) testing. There are several brands of LISN that are recognized in the market. However, the quality of measurement provided by the LISN is not dependent upon the quality of the product, but also its application scheme. This paper investigates the effect of grounding...
This paper offers a form of filtration based on moving average filter and KNN imputation method, for pre-processing hourly electricity load data for Short-Term Load Forecasting (STLF). The STLF is developed by the Adaptive Network Based Fuzzy Inference System (ANFIS). There is a lack of data pre-processing related to load forecasting, especially STLF. Unlike previous studies, to enhance the accuracy...
Due to the interrelation of different stages within the handset receiver, it is often desired to have clear requirements for the RF receiver front-end. However, the LTE standard specifies only the system-level performance, and extracting the receiver's front-end requirements is not straightforward given the complexity of current standards. These front-end requirements are important to make the right...
A near-field/far-field transformation for circular aperture antennas, formulated as a linear inverse problem, is here proposed. The approach takes into account for the a-priori information about the radiating and geometrical characteristics of the antennas under test, by representing the aperture field with Circular Prolate Spheroidal Wave functions.
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...
Identification of acoustic events is a challanging field of signal processing. Fast identification algorithms would be applicable for real-time event detection in industrial projects. Event detection is usually done by classifying a specific feature of windows of time series. This paper studies the application of the novel skeleton method for feature extraction. We compare it with traditional feature...
In this paper, we have proposed a multimodal biometric verification system adopting the physiological traits-face and fingerprint. The system has been evaluated for robustness analysis imposing the Additive White Gaussian noise (AWGN) on clean data of the employed databases-AR facial database, PolyU High-resolution fingerprint database. The unimodal and multimodal (pre and post matching fusion strategies)...
In conventional system identification and state estimation problems, it is commonly assumed that the output signal of a dynamical system is sampled at every regular time interval. This paper addresses the identification and estimation problems under the Lebesgue sampling, which is a type of event-triggered sampling such that the output signal is sampled only when it crosses a specific threshold. In...
Nowadays; Parallel corpus is one of the most important resources which can be employed in different researches such as machine translation, bilingual lexicography, and linguistics. This paper describes the process of building a large-scale (about 400, 000 sentence pairs) English-Persian parallel corpus called Tehran Parallel Corpus (TPC). The aim of study is to introduce the structure and explain...
A general methodology of device array mismatch characterization is introduced, analyzed and verified. Instead of measuring each device's parameter individually, the device array is configured as a data converter and the mismatch information is extracted from the differential linearity (DNL) of the converter. Systematic and random mismatch are characterized separately using the proposed decomposition...
Patch-based image denoising can be interpreted under the Bayesian framework which incorporates the image formation model and a prior image distribution. In the sparsity approach, the prior is often assumed to obey an arbitrarily chosen distribution. Our motivation is to estimate the probability directly from the distribution of image patches extracted from good quality images, thanks to a given dictionary...
This paper proposes the method of propagation mode extraction on ionograms with the help of the Hough transform. Usually, the Hough transform for ionogram processing is used at the step of track autoscaling, when propagation modes already extracted. Authors propose a method for image binarization based on the application of abnormal sample extraction method which allows to use of the Hough transform...
We propose herein a method to predict the sit-to-stand movement before a user leaves their seat. The proposed method is evaluated by using it for sit-to-stand and noisy movements, and the sit-to-stand movement is predicted with an average accuracy of 99.5%. Furthermore, based on this proposed method, we develop a prototype system to assist the sit-to-stand movement. To verify the effectiveness of...
Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively. However, when optimizing CNNs with SGD, the saturation behavior behind softmax always gives us an illusion of training well and then is omitted. In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of...
The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the...
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