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We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be em...
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We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we derive the capacity of sampled analog channels for three prevalent sampling strategies: sampling with filtering, sampling with filter banks, and sampling with modulation and filter banks. These sampling mechanisms subsume most nonuniform sampling techniques applied in practice. Our analyses illuminate interesting connections between undersampled channels and multiple-input multiple-output channels. The optimal sampling structures are shown to extract out the frequencies with the highest SNR from each aliased frequency set, while suppressing aliasing and out-of-band noise. We also highlight connections between undersampled channel capacity and minimum mean-squared error (MSE) estimation from sampled data. In particular, we show that the filters maximizing capacity and the ones minimizing MSE are equivalent under both filtering and filter-bank sampling strategies. These results demonstrate the effect upon channel capacity of sub-Nyquist sampling techniques, and characterize the tradeoff between information rate and sampling rate.
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This paper describes a novel inversion algorithm for determining the 3-D position coordinates and environmental complex permittivity of wireless nodes embedded in a dispersive propagation media. In this paper, wireless sensors are...
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This paper describes a novel inversion algorithm for determining the 3-D position coordinates and environmental complex permittivity of wireless nodes embedded in a dispersive propagation media. In this paper, wireless sensors are considered to form part of a network for monitoring salient parameters, such as temperature and humidity, in large wheat-filled storage vessels, where the highly reflective nature of the propagation environment engenders extreme multipath effects. Under such conditions, the inherent difficulties in deriving reliable pulse propagation delay and amplitude estimates greatly diminishes the effectiveness of conventional free-space positioning methods. These limitations have lead to the development of alternative inversion techniques capable of retrieving both geometric positioning and electromagnetic propagation data. The inversion algorithm developed within is formulated as a nonlinear least squares problem in which the misfit between time domain positioning measurements and modeled data is minimized via application of a novel minimum single-level linkage optimization algorithm. In developing a more refined propagation forward solution incorporating position coordinates, host media electromagnetic properties and antenna radiation patterns, accurate positioning resolutions and macroscopic permittivity estimates are obtained under challenging propagation conditions.
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Supervised manifold learning has been successfully applied to action recognition, in which class label information could improve the recognition performance. However, the learned manifold may not be able to well preserve both the ...
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Supervised manifold learning has been successfully applied to action recognition, in which class label information could improve the recognition performance. However, the learned manifold may not be able to well preserve both the local structure and global constraint of temporal labels in action sequences. To overcome this problem, this paper proposes a new supervised manifold learning algorithm called supervised spatio-temporal neighborhood topology learning (SSTNTL) for action recognition. By analyzing the topological characteristics in the context of action recognition, we propose to construct the neighborhood topology using both supervised spatial and temporal pose correspondence information. Employing the property in locality preserving projection (LPP), SSTNTL solves the generalized eigenvalue problem to obtain the best projections that not only separates data points from different classes, but also preserves local structures and temporal pose correspondence of sequences from the same class. Experimental results demonstrate that SSTNTL outperforms the manifold embedding methods with other topologies or local discriminant information. Moreover, compared with state-of-the-art action recognition algorithms, SSTNTL gives convincing performance for both human and gesture action recognition.
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This paper presents improvements to frequency-tracking P-class and M-class phasor measurement unit (PMU) algorithms. The performance of these algorithms is tested in simulation against the reference (basic) algorithms from the C37...
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This paper presents improvements to frequency-tracking P-class and M-class phasor measurement unit (PMU) algorithms. The performance of these algorithms is tested in simulation against the reference (basic) algorithms from the C37.118 standard, using the formal test procedures specified in that standard. The measurements of total vector error (TVE), frequency and rate-of-change-of-frequency (ROCOF) are all investigated for compliance. Generally, TVE is found to be compliant for all algorithms. By contrast there are significant excursions for frequency and ROCOF measurements. More extreme non-standard tests are also applied, involving multiple simultaneous interferences. The proposed algorithms exhibit improvements in frequency and ROCOF accuracy, with errors reduced by factors of up to 150 compared to the basic algorithms. A Hybrid P/M-class PMU is proposed and demonstrated, offering P-class response to dynamic steps but M-class steady-state accuracy. However, setting usable trigger thresholds for this device requires a thorough investigation of interharmonic effects on P-class PMUs. This investigation poses more questions than it answers, leading to a questioning of the validity of any frequency or ROCOF measurement from any P-class PMU.
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This paper establishes self-aligned double patterning (SADP) decomposition requirements and proposes a litho-friendly layout decomposition method. First, we explain the SADP-specific printability requirements that should be consid...
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This paper establishes self-aligned double patterning (SADP) decomposition requirements and proposes a litho-friendly layout decomposition method. First, we explain the SADP-specific printability requirements that should be considered during decomposition. In silico experiments indicate that layout patterns that are printed by the trim mask may experience the highest levels of image distortion. Therefore, these patterns should be assisted by sidewalls of spacer patterns to improve printing robustness. Next, we present an integer linear programming-based decomposition method that avoids decomposition conflicts and sensitive trim edges simultaneously. To improve runtime and extend its application to partially decomposable layouts, too, a partitioning-based decomposition is also proposed. Our experiments reveal that the proposed methods decrease the total length of sensitive trim patterns and consequently reduce the total edge placement error significantly.
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Fibrosis is thought to play an important role in the formation and maintenance of atrial fibrillation (AF). The propensity of fibrosis to increase AF vulnerability depends not only on its amount, its texture plays a crucial role a...
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Fibrosis is thought to play an important role in the formation and maintenance of atrial fibrillation (AF). The propensity of fibrosis to increase AF vulnerability depends not only on its amount, its texture plays a crucial role as well. While the detection of fibrotic tissue patches in the atria with extracellular recordings is feasible based on the analysis of electrogram fractionation, as used in clinical practice to identify ablation targets, the classification of fibrotic texture is a more challenging problem. This study seeks to establish a method for the electroanatomical characterization of the fibrotic textures based on the analysis of electrogram fractionation. The proposed method exploits the dependence of fractionation patterns on the incidence direction of wavefronts which differs significantly as a function of texture. A histologically detailed computer model of the right atrial isthmus was developed for testing the method. A stimulation protocol was conceived which generated various incidence directions for any given recording site where electrograms were computed. A classification method is derived then for discriminating three types of fibrosis, no fibrosis (control), diffuse, and patchy fibrosis. Simulation results showed that electrogram fractionation and amplitudes and their dependence upon incidence direction allow a robust discrimination between different classes of fibrosis. Finally, to minimize the technical effort, sensitivity analysis was performed to identify a minimum number of incidence directions required for robust classification.
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The first of this two-paper series proposes a methodology to characterize generation investment equilibria in a pool-based network-constrained electricity market, where the producers behave strategically. To this end, the investme...
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The first of this two-paper series proposes a methodology to characterize generation investment equilibria in a pool-based network-constrained electricity market, where the producers behave strategically. To this end, the investment problem of each strategic producer is represented using a bilevel model, whose upper-level problem determines the optimal investment and the supply offering curves to maximize its profit, and whose several lower-level problems represent different market clearing scenarios. This model is transformed into a mathematical program with equilibrium constraint (MPEC) through replacing the lower-level problems by their optimality conditions. The joint consideration of all producer MPECs, one per producer, constitutes an equilibrium problem with equilibrium constraints (EPEC). To identify the solutions of this EPEC, each MPEC problem is replaced by its Karush-Kuhn-Tucker (KKT) conditions, which are in turn linearized. The resulting mixed-integer linear system of equalities and inequalities allows determining the EPEC equilibria through an auxiliary MILP problem.
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Dynamic movements of the hand, fingers, and thumb are difficult to measure due to the versatility and complexity of movement inherent in function. An innovative approach to measuring hand kinematics is proposed and validated. The ...
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Dynamic movements of the hand, fingers, and thumb are difficult to measure due to the versatility and complexity of movement inherent in function. An innovative approach to measuring hand kinematics is proposed and validated. The proposed system utilizes the Microsoft Kinect and goes beyond gesture recognition to develop a validated measurement technique of finger kinematics. The proposed system adopted landmark definition (validated through ground truth estimation against assessors) and grip classification algorithms, including kinematic definitions (validated against a laboratory-based motion capture system). The results of the validation show 78% accuracy when identifying specific markerless landmarks. In addition, comparative data with a previously validated kinematic measurement technique show accuracy of MCP ± 10° (average absolute error (AAE) = 2.4°), PIP ± 12° (AAE = 4.8°), and DIP ± 11° (AAE = 4.8°). These results are notably better than clinically based alternative manual measurement techniques. The ability to measure hand movements, and therefore functional dexterity, without interfering with underlying composite movements, is the paramount objective to any bespoke measurement system. The proposed system is the first validated markerless measurement system using the Microsoft Kinect that is capable of measuring finger joint kinematics. It is suitable for home-based motion capture for the hand and, therefore, achieves this objective.
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This paper analyzes numerically the approach reported in the companion paper for identifying generation investment equilibria in an electricity market where the producers behave strategically. To this end, a two-node illustrative ...
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This paper analyzes numerically the approach reported in the companion paper for identifying generation investment equilibria in an electricity market where the producers behave strategically. To this end, a two-node illustrative example and a large-scale case study based on the IEEE reliability test system (RTS) are examined and the results obtained are reported and discussed.
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We consider a strategic producer that trades its energy in a multi-period network-constrained electricity pool and, for strategic reasons, is interested in identifying its rival producers' offer prices. Considering industry practi...
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We consider a strategic producer that trades its energy in a multi-period network-constrained electricity pool and, for strategic reasons, is interested in identifying its rival producers' offer prices. Considering industry practice, we assume that the strategic producer has knowledge of the daily market outcomes, i.e., energy quantities sold/bought and resulting locational marginal prices (LMPs) for each time period and all nodes of the network. Using this information we formulate an inverse optimization problem that allows estimating the rival producers' offer prices that have been marginal at any of the time periods under study. Such problem is well behaved, effectively identifies rival offer prices and can be efficiently solved. The effectiveness of the proposed technique is illustrated through a simple example and a realistic case study.
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