A methodology for designing optimal finite impulse response estimators with quantized coefficients is proposed. The problem of minimizing the covariance of the estimation error is translated into a constrained quadrat...
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A methodology for designing optimal finite impulse response estimators with quantized coefficients is proposed. The problem of minimizing the covariance of the estimation error is translated into a constrained quadratic Regulator problem, where the inputs need to be chosen from a finite set. Solving it has a computational complexity which is exponential in the filter impulse response length. To overcome the associated computational burden, we propose an approximate algorithm using receding horizon ideas borrowed from the Model Predictive Control framework. Of course, this approach can be independently motivated without utilizing the control analogy. We present a closed form expression for the exact solution to the problem. An example illustrates the trade-off that arises between computational time and performance.
The paper presents a collection of 93 different bugs, detected in formal verification of 65 student designs that include: 1) single-issue pipelined DLX processors;2) extensions with exceptions and branch prediction;an...
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The paper presents a collection of 93 different bugs, detected in formal verification of 65 student designs that include: 1) single-issue pipelined DLX processors;2) extensions with exceptions and branch prediction;and 3) dual-issue superscalar implementations. The processors were described in a high-level HDL, and were formally verified with an automatic tool flow. The bugs are analyzed and classified, and can be used in research on microprocessor testing.
A stability result for a recently proposed suboptimal receding horizon control (RHC) strategy for constrained linear systems is presented. The strategy is based on a singular value decomposition (SVD) of the Hessian o...
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A stability result for a recently proposed suboptimal receding horizon control (RHC) strategy for constrained linear systems is presented. The strategy is based on a singular value decomposition (SVD) of the Hessian of the quadratic performance index generally minimized in Model Predictive Control. A basis function expansion of the unconstrained optimal control vector in terms of the Hessian's singular vectors is considered. At each sampling time, the strategy computes a feasible control sequence by selec.ing a variable subset of the basis representation. Stability is analysed in the framework of optimal RHC: a final set constraint is used in combination with a stability enforced algorithm that implements the SVD- RHC strategy.
Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on immense amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of...
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ISBN:
(纸本)0889863946
Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on immense amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi-tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called ST-Cube(Spatio-Temporal Cube). The ST-Cube is an adaptive cell based index structure to handle aggregation queries over spatio-temporal data. Through the extensive performance studies, we observed that the ST-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree based frameworks.
The second-order small-slope approximation (SSA) model of elec.romagnetic rough surface scattering has been applied to a series of surface profiles representing the time-evolution of the crest of a spilling-breaker wa...
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The second-order small-slope approximation (SSA) model of elec.romagnetic rough surface scattering has been applied to a series of surface profiles representing the time-evolution of the crest of a spilling-breaker wave. The crest profiles were measured directly in a wave tank using an optical measurement technique. The SSA results were compared directly with reference scattering found using a moment-method based numerical technique and the standard two-scale model (TSM) at various angles of incidence. Both SSA and TSM give excellent results with profiles where no post-breaking steep features remain. As the incidence angle increases both SSA and TSM lose accuracy as expected. Both models give fair accuracy at vertical polarization with 80° incidence (10° grazing). However, at horizontal polarization SSA dramatically underpredicts the scattering during most of the measurement time. TSM shows better performance under these conditions, provided that the appropriate scale-separation threshold is used.
By introducing psycho-acoustical aspects, the audio quantization problem is translated into a finite horizon optimal control problem with a finite input constraint set. An explicit solution is obtained. Its receding h...
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By introducing psycho-acoustical aspects, the audio quantization problem is translated into a finite horizon optimal control problem with a finite input constraint set. An explicit solution is obtained. Its receding horizon implementation gives rise to the Receding Horizon Quantizer (RHQ). The RHQ corresponds to a closed loop, which embeds the widely-adopted Noise Shaping Quantizer in a broader framework and allows for enhanced performance. Furthermore, suboptimal algorithms are described and their behaviour illustrated by means of an example.
Mobile Ad-hoc networks are inherently prone to security attacks, with node mobility being the primary cause in allowing security breaches. This makes the network susceptible to Byzantyne faults with packets getting mi...
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Mobile Ad-hoc networks are inherently prone to security attacks, with node mobility being the primary cause in allowing security breaches. This makes the network susceptible to Byzantyne faults with packets getting misrouted, corrupted or dropped. In this paper we propose solutions using an unobtrusive monitoring technique using the "Detection Manager" to locate malicious or faulty nodes that misroute, corrupt or drop packets. The unobtrusive monitoring technique is similar to an intrusion detection system that monitors system activity logs to determine if the system is under attack. This technique uses information from different network layers to detect malicious nodes. The detection manager we are developing for Mobile Ad-hoc networks stores several rules for responding to different situations. Any single node in the network can use unobtrusive monitoring without relying on the cooperation of other nodes, which makes unobtrusive monitoring easy to implement and deploy. Simulations of Mobile Ad-hoc networks that contain malicious nodes indicate that unobtrusive monitoring has a high detection effectiveness with low false positive rate.
We propose a hierarchical 3D facial modeling technique for realistic facial expression animation from the anatomical specification. We analyze the real human face to construct the facial model with a hierarchical biom...
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We propose a hierarchical 3D facial modeling technique for realistic facial expression animation from the anatomical specification. We analyze the real human face to construct the facial model with a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different kinds of muscle models have been developed to simulate the distribution of the muscle force exerted to the skin due to muscle contraction. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle construction. The facial muscle construction is achieved by a muscle mapping approach which efficiently locates facial muscles at the anatomically correct positions between the skin and skull layers. To generate facial expressions, the resulting hierarchical facial model is animated by solving the governing dynamic equation. For computational efficiency, we use a localized, semi-implicit integration method in the numerical simulation. The dynamic facial animation algorithm runs at an interactive rate and synthesizes various realistic facial expressions.
In this paper, the authors propose two new evolutionary approaches to Multiobjective Optimization Problems (MOPs) - Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA)...
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In this paper, the authors propose two new evolutionary approaches to Multiobjective Optimization Problems (MOPs) - Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA). In DPSMO, instead of using genetic operators (e.g., crossover and mutation), the information sharing technique in Particle Swarm Optimization (PSO) is applied to inform the entire population more accurate moving direction and speed as opposed to any generic evolutionary algorithms (EA). Meanwhile, based on the dynamic population strategies, cell-based rank and density estimation and objective space compression strategy used in Dynamic Mutiobjective Evolutionary Algorithm (DMOEA), the DPSMO can evolve to an approximately optimal population size while the population is approaching the true Pareto front. To overcome DPSMO's difficulty in producing a high-quality Pareto front, DPSEA is designed by combining both EA and PSO's information sharing techniques. By examining the selec.ed performance measures on one test function, DPSEA is found to be competitive with, or even superior to DMOEA and DPSMO in terms of keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front.
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