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Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for the walls' positions using the Back-Projection (BP) imaging algorithm. Then, after obtaining the BP image of target, we extract the focus regions produced by different multipaths. Finally, we obtain the position of the target based on the extracted walls' positions and the focus regions. Simulations and experiments results validate the proposed algorithm.

In this work, we present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the input and the impulse responses of the linear system are estimated using the posterior means of the Gaussian-process models given the data, and the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in simple update steps. In the most general formulation, neither the marginal likelihood nor the posterior distribution of the unknowns is tractable. Therefore, we propose two approximation approaches, one based on Markov-chain Monte Carlo techniques and one based on variational Bayes approximation. We also show special model structures for which the distributions are treatable exactly. Through numerical simulations, we study the application of the uncertain-input model to the identification of Hammerstein systems and cascaded linear systems. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as classical PEM, Hammerstein models, errors-in-variables problems, blind system identification, and cascaded linear systems. This allows us to build a systematic procedure to apply the algorithms proposed in this work to a wide class of classical problems., 27 Pages, submitted to Automatica

Meticulous objective examination of a patient with blepharoptosis allows determining the tactics of surgical treatment. It depends on many factors, but main ones are blepharoptosis etiology, upper eyelids levator function, and ptosis degree. The estimation algorithm of objective examination of a patient with blepharoptosis is presented in this article.

Compressingimage files after splitting them into certain number of parts can increasecompression ratio. Acquired compression ratio can also be increased bycompressing each part of the image using different algorithms, because each algorithmgives different compression ratios on different complexity values. In thisstudy, statistical compression results and measured complexity values of splitimages are obtained, and an estimation algorithm based on these results ispresented. Our algorithm splits images into 16 parts, compresses each part withdifferent algorithm and joins the images after compression. Compression resultsshow that using our estimation algorithm acquires higher compression ratiosover whole image compression techniques with ratio of 5% on average and 25% onmaximum., Resimdosyalarını belirli sayıda parçalara böldükten sonra sıkıştırma işlemi yapmak sıkıştırmaoranını arttırabilmektedir. Elde edilen sıkıştırma oranı resmin her parçasınınfarklı bir algoritmayla sıkıştırılması ile daha da fazla arttırılabilmektedir.Her algoritma farklı karmaşıklık değerlerinde farklı sıkıştırma oranı sağlamaktadır.Bu çalışmada bölünmüş resimlerden sıkıştırma sonuçları istatistikleri veölçülen karmaşıklık değerleri elde edilmiş ve bu sonuçları kullanan bir tahminalgoritması önerilmiştir. Algoritmamız resimleri 16 parçaya böler, her parçayıfarklı bir algoritmayla sıkıştırır ve bu parçaları en son aşamada birleştirir.Sıkıştırma sonuçlarından görüldüğü üzere algoritmamız resmi tek parça halindesıkıştırma işlemine göre ortalama %5 ve maksimum %25 daha iyi sıkıştırmaperformansı sağlamıştır.

Cataloged from PDF version of article. This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. (C) 2013 Elsevier B.V. All rights reserved.

We propose a decentralized and non cooperative algorithm for estimation and control in a multi-agent system of oscillators to achieve a balanced circular formation. Each agent gathers an uncertain measurement of its phase distance from other agents only when they are in its proximity. Based on this uncertain and intermittent data and on the a priori knowledge of the nominal (e.g. uncontrolled) agent's velocities, we employ an estimation method to reconstruct the relative angular positions. Then, we develop a bang-bang controller to achieve a balanced platoon formation. The novelty of the approach is that the balanced formation is achieved by using proximity sensors rather than distance transducers. Moreover, the bang-bang control strategy is designed so that the control goal is achieved even when the range of the sensors is lower than the desired spacing distance. The effectiveness of the approach is illustrated through extensive numerical simulations.

For estimating the missing data of wireless sensor networks, an estimation algorithm called HD method, which can make use of sensoring space-time correlation of the data, was proposed based on mathematical Hermite and DESM statistical models. The algorithm not only can adaptively adjust the time and space weights, but also can accurately estimate the missing or unavailable data. The experimental results show that the algorithm has good stability and relatively high estimation accuracy.

The problem of estimating the node density in ad hoc networks is a significant one for protocol design. In molecular nanonetworks, the node density estimation problem poses additional challenges due to the limited processing and communication capabilities of the network nodes which necessitate the design of simple to implement distributed solutions, and the diffusion based communication channel which is different from traditional electromagnetic networks. In this work, inspired by the quorum sensing process, we propose and analyze a new node density estimation scheme based on synchronous transmission of all network nodes and measurement of the received molecular concentration. We show that when the synchronous transmission is performed in infinite space, a linear parametric model of the node density can be derived which can be used for estimation purposes. When, however, the transmission is performed over a finite space the model becomes time varying. To overcome the difficulties associated with the time varying nature we propose the use of periodic transmission which for large enough values of the period transforms the linear model into a static one. An online parameter identification technique is then introduced to estimate the node density using the derived linear static parametric models. The utilization of the node density estimates to adaptively regulate probabilistic flooding in network structures relevant to nanonetworks is then considered. The random geometric graph model and uniform grid structures are used to demonstrate how the node estimates can be used to dictate the desired rebroadcast probabilities, through analysis and simulations. © 2017 Elsevier B.V. 12 43 52

Estimation algorithms for the state of an object based on "relative" objects information are proposed, under conditions of disturbances affecting the object and errors caused by incomplete and inaccurate measurements of the object's vector. Information on disturbances and measurement errors is considered known up to some given sets. If the path of the object's motion is changed, it is suggested to use information provided by a group of similar objects for identifying model of the object's further motion. © Pleiades Publishing, Ltd., 2010.

The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation.

Conference name: 4th International Conference, MobiCASE 2012 Date of Conference: 11-12 October 2012 This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes. © 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

New and upcoming exhaust emissions regulations and fuel consumption reduction requirements are forcing the development of innovative and particularly complex intake-engine-exhaust layouts. Especially in the case of Compression Ignition (CI) engines, the HC-CO-NOx-PM after-treatment system is becoming extremely expensive and sophisticated, and the necessity to further reduce engine-out emission levels, without significantly penalizing fuel consumption figures, may lead to the adoption of intricate and challenging intake-exhaust systems configurations. The adoption of both long- and short-route Exhaust Gas Recirculation (EGR) systems is one example of such situation, and the need to precisely measure (or estimate) mass flow rates in the various elements of the gas exchange circuit is one of the consequences. Within this context, the paper presents an innovative solution for real-time estimation of the exhaust gas mass flow rate of a modern Turbo-Diesel Engine, equipped with Variable Geometry Turbine (VGT), Diesel Particulate Filter (DPF), and EGR.

Many congestion control protocols have been recently proposed in order to alleviate the problems encountered by TCP in high-speed networks and wireless links. Protocols utilizing an architecture that is in the same spirit as the ABR service in ATM networks require estimates of the effective number of users utilizing each link in the network to maintain stability in the presence of delays. In this paper, we propose a novel estimation algorithm that is based on online parameter identification techniques and is shown through analysis and simulations to converge to the effective number of users utilizing each link. The algorithm does not require maintenance of per-flow states within the network or additional fields in the packet header, and it is shown to outperform previous proposals that were based on pointwise division in time. The estimation scheme is designed independently from the control functions of the protocols and is thus universal in the sense that it operates effectively in a number of congestion control protocols. It can thus be successfully used in the design of new congestion control protocols. In this paper, to illustrate its universality, we use the proposed estimation scheme to design a representative set of Internet congestion control protocols. Using simulations, we demonstrate that these protocols satisfy key design requirements. They guide the network to a stable equilibrium that is characterized by high network utilization, small queue sizes, and max-min fairness. In addition, they are scalable with respect to changing bandwidths, delays, and number of users, and they generate smooth responses that converge quickly to the desired equilibrium. © 2011 IEEE. 19 5 1499 1512 Cited By :8

In this paper we propose a new method to estimate nonparametrically a time varying parameter model when some qualitative information from outside data (e.g. seasonality) is available. In this framework we make two main contributions. First, the resulting estimator is shown to belong to the class of generalized ridge estimators and under some conditions its rate of convergence is optimal within its smoothness class. Furthermore, if the outside data information is fullfilled by the underlying model, the estimator shows efficiency gains in small sample sizes. Second, for the implementation process, since the estimation procedure envolves the computation of the inverse of a high order matrix we provide an algorithm that avoids this computation and, also, a data-driven method is derived to select the control parameters. The practical performance of the method is demonstrated in a simulation study and in an application to the demand of soft drinks in Canada. This work was supported by Dirección General de Enseñanza Superior del Ministerio de Educación y Ciencia under research grant PB98-0149, and by the Universidad del País Vasco under research grant UPV 038.321-HA129/99.

It is a well known fact that a major threat to GNSS applications is represented by interference: both unintentional and deliberate interference may compromise accuracy, integrity, continuity and availability of Position Velocity and Time (PVT) solutions. In particular continuous/swept wave jammers, being really cheap and easy to implement, are one of the most widespread and dangerous interference source. In order to fulfill application requirements, proper interference cancellation and mitigation techniques must be devised. In this paper, we present a distributed-sensing signal waveform estimation algorithm (DSWE) based on Karhunen-Loéve Expansion (KLE). Monte Carlo simulations are used for performance evaluation and show the effectiveness of the proposed technique.