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This paper focuses on the impacts of considering batteries state-of-charge (SOC) at the departure time on the demand modeling of plug-in electric vehicles (PEVs). Almost all of the previous researches assumed that PEVs batteries at the departure time are fully charged; however, this assumption is highly questionable because it is probable for a PEV to not be charged every day. The probability density...
We consider the joint source-channel coding (JSCC) problem where the real valued outputs of two correlated memoryless Gaussian sources are scalar quantized, bit assigned, and transmitted, without applying any error correcting code, over a multiple access channel (MAC) which consists of two orthogonal point-to- point time-correlated Rayleigh fading sub-channels with soft- decision demodulation. At...
Considering the fact that when the signal-to-noise ratio (SNR) is less than a certain threshold, the bit error rate (BER) performance of joint source-channel decoding (JSCD) will become more vulnerable to channel noise. To further improve the performance of JSCD, a practical and efficient algorithm, named joint iterative source-channel decoding (JISCD), is proposed in this paper, in which the performance...
In this paper, we present an extension of the semi-definite programming formulation of the optimal rate code design in single link Binary Erasure Channel (BEC) proposed by the authors to the Binary Erasure Multiple Access Channel (BE-MAC) with two sources correlation. This new way can be easily extended to the multiple access senders. Simulation results show the efficiency and effectiveness of the...
Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint...
Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation...
We present a semi-supervised learning algorithm based on local and global consistency, working on a bi-relational graph of images and labels. By incorporating two types of different entities (images and labels) in a single graph, label propagation can exploit label correlations for measuring the relevance between unannotated images and labels, leading to a significant improvement in performance. In...
In this paper, imputation algorithm based on Gaussian copula in time series data is given. The case study is a missing value of 33 years Gross Development Product (GDP) of nine countries (from 1950 to 1983). The missing value was predicted by error model of autoregressive (AR) model assumed following N(μ, σ2)distribution. Since the data is time series and modeled with AR, the recent data is influenced...
Time domain reflectometry (TDR) applied to live cables is hampered by the absence of a clear reflection from the far end. Instead, location of partial discharge (PD) can be extracted from small structures in recorded patterns arising from any impedance variation along the signal propagation channel. This paper explores two approaches for a single-sided PD location system. The first method compares...
Due to changes in the electric power system, small, distributed units substitute conventional power plants with regard to energy supply. This also holds for the provision of ancillary services. To this end, agents that represent units form coalitions. In this paper, a method is introduced to assess such coalitions with respect of how reliable they can provide ancillary services taking into account...
Considering the fact that the wireless sensor networks (WSNs) need to maintain a long lifetime, there is a great demand to decrease energy dissipation of the sensor. Data compression is an efficient method to solve the problem. This paper proposes a practical and efficient data compression algorithm with high compression and noise-resisted features, in which the quasi-cyclic low-density parity-check...
In this paper we propose a novel multiple target tracking model composed of two detectors and a tracker. An on-line detector and a tracker are used to generate target candidates, whose confidence scores are then evaluated by the off-line trained detectors. In the data association stage, the high-efficient inference in a structural model leads to the optimal tracking result. The experimental results...
Distributed compressed sensing (DCS) is able to exploit both intra-and inter-signal correlation structures of multi-signal ensemble. This paper proposes a DCS scheme for image signal compression and reconstruction. The key idea is to exploit the inter-correlation of the blocks that split from the image. Significantly, joint sparse model was employed to compress the intra- and inter-redundancy of the...
The vast amounts of data which can be collected using body-sensor networks with high temporal and spatial resolution require a novel analysis approach. In this context, state-of-the-art Bayesian approaches based on variational, non-parametric or MCMC derived methods often become computationally intractable when faced with several million data points. Here, we present how the simple combination of...
The paper presents methods to analyze approaches concerned with application of information theoretic techniques in such a branch of the control theory as system identification: application of the mutual (Shannon) information and attempts of generalization of the notion of entropy, as well as application of consistent measures of dependence based on the information-theoretic (Kulback-Leibler) divergence...
Recent generalisations of stochastic filtering methods to multi-object systems have become very popular for solving multi-target tracking problems over the last decade. However, there was previously no general means of introducing correlations between objects. In this article, we investigate generalisations of such multi-object filters for systems where there may be dependencies between objects. Determining...
In this paper we address the problem of cognition (inference and decision) on data collected from a system which is characterized by a stochastic network topology. The decision problem is formulated as a data fusion problem and a data model that only requires stochastic network topology, marginal probability densities and pairwise correlations is proposed. Based on such model, we can mitigate the...
Missing data cases are a problem in all types of statistical analyses and arise in almost all application domains. Several schemes have been studied in this paper to overcome the drawbacks produced by missing values in data mining tasks, one of the most well known is based on pre processing, formerly known as imputation. In this work, we propose a new multiple imputation approach based on sampling...
Compressive sampling (CS) aims at acquiring a signal at a sampling rate below the Nyquist rate by exploiting prior knowledge that a signal is sparse or correlated in some domain. Despite the remarkable progress in the theory of CS, the sampling rate on a single image required by CS is still very high in practice. In this paper, a non-local compressive sampling (NLCS) recovery method is proposed to...
Many analytical metrics have been proposed to evaluate the quality of a grasp based on different criteria and principles. To use most of them in practical real applications, some operational parameters need to be determined: maximum and minimum values, normalization ratios, quality thresholds, robustness in front of position errors and, more importantly, relations between alternative metrics. This...
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