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A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the con...
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A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm–mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a pro- f that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.
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While every network node only relays messages in a traditional communication system, the recent network coding (NC) paradigm proposes to implement simple in-network processing with packet combinations in the nodes. NC extends the ...
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While every network node only relays messages in a traditional communication system, the recent network coding (NC) paradigm proposes to implement simple in-network processing with packet combinations in the nodes. NC extends the concept of “encoding” a message beyond source coding (for compression) and channel coding (for protection against errors and losses). It has been shown to increase network throughput compared to traditional networks implementation, to reduce delay and to provide robustness to transmission errors and network dynamics. These features are so appealing for multimedia applications that they have spurred a large research effort towards the development of multimedia-specific NC techniques. This paper reviews the recent work in NC for multimedia applications and focuses on the techniques that fill the gap between NC theory and practical applications. It outlines the benefits of NC and presents the open challenges in this area. The paper initially focuses on multimedia-specific aspects of network coding, in particular delay, in-network error control, and media-specific error control. These aspects permit to handle varying network conditions as well as client heterogeneity, which are critical to the design and deployment of multimedia systems. After introducing these general concepts, the paper reviews in detail two applications that lend themselves naturally to NC via the cooperation and broadcast models, namely peer-to-peer multimedia streaming and wireless networking.
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In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can...
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In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.
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We bound the minimum number $w$ of wires needed to compute any (asymptotically good) error-correcting code $C:{0,1}^{Omega (n)}to{0,1}^{n}$ with minimum distance $Omega (n)$, using unbounded fan-in circuits of depth $d$ with arbi...
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We bound the minimum number $w$ of wires needed to compute any (asymptotically good) error-correcting code $C:{0,1}^{Omega (n)}to{0,1}^{n}$ with minimum distance $Omega (n)$, using unbounded fan-in circuits of depth $d$ with arbitrary gates. Our main results are: 1) if $d=2$, then $w=Theta (n ({lg n/lglg n})^{2})$; 2) if $d=3$, then $w=Theta (nlglg n)$; 3) if $d=2k$ or $d=2k+1$ for some integer $kgeq 2$, then $w=Theta (nlambda_{k}(n))$, where $lambda_{1}(n)=lceillg nrceil$, $lambda_{i+1}(n)=lambda_{i}^{ast}(n)$, and the $ast$ operation gives how many times one has to iterate the function $lambda_{i}$ to reach a value at most 1 from the argument $n$; and 4) if $d=lg^{ast}n$, then $w=O(n)$. For de- th $d=2$, our $Omega (n ({lg n/lglg n})^{2})$ lower bound gives the largest known lower bound for computing any linear map. The upper bounds imply that a (necessarily dense) generator matrix for our code can be written as the product of two sparse matrices. Using known techniques, we also obtain similar (but not tight) bounds for computing pairwise-independent hash functions. Our lower bounds are based on a superconcentrator-like condition that the graphs of circuits computing good codes must satisfy. This condition is provably intermediate between superconcentrators and their weakenings considered before.
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Seeking sound samples in a massive database can be a tedious and time consuming task. Even when metadata are available, query results may remain far from the timbre expected by users. This problem stems from the nature of query sp...
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Seeking sound samples in a massive database can be a tedious and time consuming task. Even when metadata are available, query results may remain far from the timbre expected by users. This problem stems from the nature of query specification, which does not account for the underlying complexity of audio data. The Query By Example (QBE) paradigm tries to tackle this shortcoming by finding audio clips similar to a given sound example. However, it requires users to have a well-formed soundfile of what they seek, which is not always a valid assumption. Furthermore, most audio-retrieval systems rely on a single measure of similarity, which is unlikely to convey the perceptual similarity of audio signals. We address in this paper an innovative way of querying generic audio databases by simultaneously optimizing the temporal evolution of multiple spectral properties. We show how this problem can be cast into a new approach merging multiobjective optimization and time series matching, called MultiObjective Time Series (MOTS) matching. We formally state this problem and report an efficient implementation. This approach introduces a multidimensional assessment of similarity in audio matching. This allows to cope with the multidimensional nature of timbre perception and also to obtain a set of efficient propositions rather than a single best solution. To demonstrate the performances of our approach, we show its efficiency in audio classification tasks. By introducing a selection criterion based on the hypervolume dominated by a class, we show that our approach outstands the state-of-art methods in audio classification even with a few number of features. We demonstrate its robustness to several classes of audio distortions. Finally, we introduce two innovative applications of our method for sound querying.
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We consider a multiple-input multiple-output (MIMO) interference channel (IC), where a single data stream per user is transmitted and each receiver treats interference as noise. The paper focuses on the open problem of computing t...
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We consider a multiple-input multiple-output (MIMO) interference channel (IC), where a single data stream per user is transmitted and each receiver treats interference as noise. The paper focuses on the open problem of computing the outermost boundary (so-called Pareto boundary-PB) of the achievable rate region under linear transceiver design. The Pareto boundary consists of the strict PB and non-strict PB. For the two user case, we compute the non-strict PB and the two ending points of the strict PB exactly. For the strict PB, we formulate the problem to maximize one rate while the other rate is fixed such that a strict PB point is reached. To solve this non-convex optimization problem which results from the hard-coupled two transmit beamformers, we propose an alternating optimization algorithm. Furthermore, we extend the algorithm to the multi-user scenario and show convergence. Numerical simulations illustrate that the proposed algorithm computes a sequence of well-distributed operating points that serve as a reasonable and complete inner bound of the strict PB compared with existing methods.
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In this paper, the problem of stability on stochastic systems with state-dependent switching is investigated. To analyze properties of the switched system by means of Itô's formula and Dynkin's formula, it is critical to show swi...
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In this paper, the problem of stability on stochastic systems with state-dependent switching is investigated. To analyze properties of the switched system by means of Itô's formula and Dynkin's formula, it is critical to show switching instants being stopping times. When the given active-region set can be replaced by its interior, the local solution of the switched system is constructed by defining a series of stopping times as switching instants, and the criteria on global existence and stability of solution are presented by Lyapunov approach. For the case where the active-region set can not be replaced by its interior, the switched systems do not necessarily have solutions, thereby quasi-solution to the underlying problem is constructed and the boundedness criterion is proposed. The significance of this paper is that all the results presented depend on some easily-verified assumptions that are as elegant as those in the deterministic case, and the proofs themselves provide design procedures for switching controls.
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When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori ...
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When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori known, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, the distribution is unknown, motivating the use of robust algorithms like LASSO—which is nearly minimax optimal—at the cost of significantly larger MSE for non-least-favorable distributions. As an alternative, we propose an empirical-Bayesian technique that simultaneously learns the signal distribution while MMSE-recovering the signal—according to the learned distribution—using AMP. In particular, we model the non-zero distribution as a Gaussian mixture and learn its parameters through expectation maximization, using AMP to implement the expectation step. Numerical experiments on a wide range of signal classes confirm the state-of-the-art performance of our approach, in both reconstruction error and runtime, in the high-dimensional regime, for most (but not all) sensing operators.
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We propose a particle filtering technique to track multiple maneuvering targets in the presence of clutter. We treat data association and state estimation, which are the two important sub-problems in tracking, as separate problems...
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We propose a particle filtering technique to track multiple maneuvering targets in the presence of clutter. We treat data association and state estimation, which are the two important sub-problems in tracking, as separate problems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. Further, in contrast to the traditional Monte-Carlo data association algorithms that use samples of the association vector obtained from a proposal distribution, our method finds the association in a deterministic fashion. We then use Monte-Carlo sampling on the reduced dimensional state of each target, independently, and thereby mitigate the curse-of-dimensionality problem that is known to occur in particle filtering. We provide a number of numerical results to demonstrate the performance of our proposed filtering algorithm.
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This paper analyzes the dynamics of an offshore boom crane and proposes a high-performance nonlinear controller to drive the system states to track some constructed trajectories. Specifically, by employing Lagrange's method in an ...
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This paper analyzes the dynamics of an offshore boom crane and proposes a high-performance nonlinear controller to drive the system states to track some constructed trajectories. Specifically, by employing Lagrange's method in an attached frame, a dynamic model is obtained for the offshore crane system consisting of the boom and a payload, with specific emphasis on the effect of the vessel's motion on the payload swing. Based on the model, a novel nonlinear control law is designed for the underactuated boom crane, which makes the system states track some planned trajectories successfully, even in the presence of persistent disturbance in harsh sea conditions. The stability of the designed closed-loop system is proven by Lyapunov techniques. Simulation and experimental results are included to demonstrate that the proposed control method significantly reduces the impact of the disturbance in harsh sea conditions.
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