LAGER is an integrated computer-aided design (CAD) system for algorithm-specific integrated circuit (IC) design, targeted at applications such as speech processing, image processing, telecommunications, and robot cont...
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LAGER is an integrated computer-aided design (CAD) system for algorithm-specific integrated circuit (IC) design, targeted at applications such as speech processing, image processing, telecommunications, and robot control. LAGER provides user interfaces at behavioral, structural, and physical levels and allows easy integration of new CAD tools. LAGER consists of a behavioral mapper and a silicon assembler. The behavioral mapper maps the behavior onto a parameterized structure to produce microcode and parameter values. The silicon assembler then translates the filled-out structural description into a physical layout and with the aid of simulation tools, the user can fine tune the data path by iterating this process. The silicon assembler can also be used without the behavioral mapper for high sample rate applications. A number of algorithm-specific IC's designed with LAGER have been fabricated and tested, and as examples, a robot arm controller chip and a real-time image segmentation chip will be described.
Several high-level-synthesis users, whose experience spans the range of commercially available HLS tools, were recently invited to a virtual roundtable to share their HLS experiences. The various questions provide con...
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Several high-level-synthesis users, whose experience spans the range of commercially available HLS tools, were recently invited to a virtual roundtable to share their HLS experiences. The various questions provide context for how they have used HLS, the benefits they have derived from it, and areas for improvement that they would like to see in the future.
A parallel higher-order method of moments (HOMoM) with a newly developed reduced-communication, lower-upper (RCLU) decomposition solver is proposed in this article. The method uses 201,600 central processing unit (CPU...
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A parallel higher-order method of moments (HOMoM) with a newly developed reduced-communication, lower-upper (RCLU) decomposition solver is proposed in this article. The method uses 201,600 central processing unit (CPU) cores on a supercomputer located in Guangzhou, China. Our code achieves an extremely high parallel efficiency when simulating a large aircraft that has been discretized in the method-of-moments (MoM) context, using a higher-order quadrilateral patch basis, into approximately 1.06 million unknowns for the surface-current distribution. Remarkably, its solution using the classical lower-upper (LU) solver only takes roughly half an hour. In addition, a review of the in-core and out-of-core algorithms of an HOMoM is presented, with a focus on their parallel implementation. The parallel performance of the methodology is also demonstrated on some challenging applications.
A new formulation of the detection filter problem is generated by assignment of the closed-loop eigenstructure under certain constraints. Detection filters, which are actually a specific class of observers, fix the ou...
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A new formulation of the detection filter problem is generated by assignment of the closed-loop eigenstructure under certain constraints. Detection filters, which are actually a specific class of observers, fix the output error direction of the system so that it can be associated with a particular failure mode and its known design failure direction. The derivation of detection filters from an eigensystem assignment approach permits a very transparent theory. The detection filter gains and closed-loop eigenvectors are obtained from a set of simultaneous equations. Necessary and sufficient conditions for the solution of these algebraic equations are determined which produce a complete theory for detection filters.
Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This te...
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Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is, therefore, relevant for the near-optimal control of nonlinear switched systems for which the switching signal is the control, and no continuous input is present. However, OP exhibits several limitations, which prevent its desired application in a standard control engineering context, as it requires, for instance, that the stage cost takes values in [0.1], an unnatural prerequisite, and that the cost function is discounted. In this article, we modify OP to overcome these limitations, and we call the new algorithm OPmin. We then analyze OPmin under general stabilizability and detectability assumptions on the system and the stage cost. New near-optimality and performance guarantees for OPmin are derived, which have major advantages compared to those originally given for OP. We also prove that a system whose inputs are generated by OPmin in a receding-horizon fashion exhibits stability properties. As a result, OPmin provides a new tool for the near-optimal, stable control of nonlinear switched discrete-time systems for generic cost functions.
Block modified covariance algorithms are proposed for autoregressive (AR) parametric spectral estimation. First, we develop the block modified covariance algorithm (BMCA) which can be implemented either in the time or...
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Block modified covariance algorithms are proposed for autoregressive (AR) parametric spectral estimation. First, we develop the block modified covariance algorithm (BMCA) which can be implemented either in the time or in the frequency domain-with the latter being more efficient in high-order cases. A block algorithm is also developed for the energy weighted combined forward and backward prediction. This algorithm is called energy weighted BMCA (EWBMCA) and its performance is analogous to that of the weighted covariance method proposed by Nikias and Scott. Time-varying convergence factors, designed to minimize the error energy from one iteration to the next, are given for both algorithms. In addition, three updating schemes are presented, namely block-by-block, sample-by-sample, and sample-by-sample with time-scale separation. The performance of the proposed algorithms is examined with stationary and nonstationary narrowband and broadband processes, and also with sinusoids in noise. Lastly, we discuss the computational complexity of the proposed algorithms and we give performance comparisons to existing modified covariance algorithms.
An efficient two-dimensional finite-difference time-domain (2-D FDTD) method combined with an autoregressive (AR) signal analysis has been proposed thr analyzing the propagation properties of microwave guiding structu...
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An efficient two-dimensional finite-difference time-domain (2-D FDTD) method combined with an autoregressive (AR) signal analysis has been proposed thr analyzing the propagation properties of microwave guiding structures, The method is especially suitable for analyzing lossy transmission lines;and in contrast with previous approaches, it is based on an algorithm of a real domain only, The algorithm is verified by comparing the numerical results with exact solutions for dielectric loaded rectangular waveguides. The conductor losses in a variety of microstrip lines and coplanar waveguides have been accurately estimated by solving the electromagnetic fields in the conductors directly.
The integration of heterogeneous aviation information networks (HAIN) has recently attracted significant attention among researchers. An important topic requiring discussion is the method by which timely and accurate ...
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The integration of heterogeneous aviation information networks (HAIN) has recently attracted significant attention among researchers. An important topic requiring discussion is the method by which timely and accurate information may be acquired to ensure aviation safety and facilitate risk evaluation. This paper proposes a distributed gateway clustering framework, whereby gateways collaborate and cooperate with each other to achieve load balancing and cooperative communication for the integration of HAIN. Unlike traditional approaches, in the framework, a cooperative architecture is presented for HAIN interoperability and load preference is taken into account to cater to the specialized nature of HAIN through describing load matrix. Two approaches are proposed for load allocation: 1) the load preference allocation (LPA) algorithm at the subnet level in which each subnet is controlled by the same gateway with predictive load assignment by incorporating the historical load information of each subnet;and 2) the gateway cooperative load allocation (GCLA) algorithm at the gateway level aimed to balance the distribution of traffic load among gateways globally. The related parameters of operating efficiency and processing time are used to analyze and evaluate the performance of the proposed load allocation algorithms of the integrated HAIN system. Simulation results are presented to show the effectiveness of the proposed framework.
Rear-end collision avoidance relies on mathematical models to calculate the safety distance. Vehicle deceleration is a key parameter for the accuracy of the models. Current models, however, assume a constant decelerat...
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Rear-end collision avoidance relies on mathematical models to calculate the safety distance. Vehicle deceleration is a key parameter for the accuracy of the models. Current models, however, assume a constant deceleration during braking, which is unrealistic. This assumption results in large over-approximation / under-approximation. In this paper, we rectify this limitation by proposing a new model that accounts for realistic vehicle deceleration during braking. Simulation results show that our approach guarantees safety. Moreover, traffic flow is improved by 21.6% compared to the widely adopted the Berkeley algorithm.
We study a class of random sampling-based algorithms for solving general non-differentiable optimization problems. These are iterative approaches that are based on sampling from and updating an underlying distribution...
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We study a class of random sampling-based algorithms for solving general non-differentiable optimization problems. These are iterative approaches that are based on sampling from and updating an underlying distribution function over the set of feasible solutions. In particular, we propose a novel and systematic framework to investigate the convergence and asymptotic convergence rates of these algorithms by exploiting their connections to the well-known stochastic approximation ( SA) method. Such an SA framework unifies our understanding of these randomized algorithms and provides new insight into their design and implementation issues. Our preliminary numerical experiments indicate that new implementations of these algorithms based on the proposed framework may lead to improved performance over existing procedures.
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