Advanced methods of fault detection and diagnosis become increasingly important for the improvement of reliability, safety and efficiency in nanoscale designs. Because the existing approaches do not give a deeper insi...
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Advanced methods of fault detection and diagnosis become increasingly important for the improvement of reliability, safety and efficiency in nanoscale designs. Because the existing approaches do not give a deeper insight and usually do not allow a comprehensive fault diagnosis, multi-level model based methods of fault detection were developed by using hierarchy of detection and diagnosis methods. This contribution proposes a memory physical (scrambling) aware multi-level fault diagnosis flow which is generic and applicable both for planar- and FinFET-based memories. In addition, special test algorithms for classification of static and dynamic faults are discussed while for classification of FinFET-specific faults a new test algorithm March FFDD is proposed. The flow is validated on 16nm FPGA board as well as it has been applied to numerous chips enabling successful physical failure analysis (PFA). At the end of the paper some real-life case scenarios of the flow application are presented.
One of the drawbacks of the Fourier reconstruction-based approach is the lack of good, efficient interpolation schemes to convert from the polar grid to the Cartesian grid that are needed to do the inverse FFT. Siemen...
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One of the drawbacks of the Fourier reconstruction-based approach is the lack of good, efficient interpolation schemes to convert from the polar grid to the Cartesian grid that are needed to do the inverse FFT. Siemens Computerized Tomography group has developed an interpolation scheme that addresses such a shortcoming. In this article we present a parallel approach for a Fourier reconstruction-based method that uses this interpolation scheme. We also present a parallel approach of the rebinning method used with the approach. The rebinning method is used to convert the acquired fan projections into parallel projections. We give an overview of the method, provide the mapping approach proposed to parallelize the algorithms, present a brief description of the architecture used in the simulation, describe the simulation model, and give the simulation results.
This paper studies the multivehicle task assignment problem where several dispersed vehicles need to visit a set of target locations in a time-invariant drift field while trying to minimize the total travel time. Usin...
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This paper studies the multivehicle task assignment problem where several dispersed vehicles need to visit a set of target locations in a time-invariant drift field while trying to minimize the total travel time. Using optimal control theory, we first design a path planning algorithm to minimize the time for each vehicle to travel between two given locations in the drift field. The path planning algorithm provides the cost matrix for the target assignment, and generates routes once the target locations are assigned to a vehicle. Then, we propose several clustering strategies to assign the targets, and we use two metrics to determine the visiting sequence of the targets clustered to each vehicle. Mainly used to specify the minimum time for a vehicle to travel between any two target locations, the cost matrix is obtained using the path planning algorithm, and is in general asymmetric due to time-invariant currents of the drift field. We show that one of the clustering strategies can obtain a min-cost arborescence of the asymmetric target-vehicle graph where the weight of a directed edge between two vertices is the minimum travel time from one vertex to the other respecting the orientation. Using tools from graph theory, a lower bound on the optimal solution is found, which can be used to measure the proximity of a solution from the optimal. Furthermore, by integrating the target clustering strategies with the target visiting metrics, we obtain several task assignment algorithms. Among them, two algorithms guarantee that all the target locations will be visited within a computable maximal travel time, which is at most twice of the optimal when the cost matrix is symmetric. Finally, numerical simulations show that the algorithms can quickly lead to a solution that is close to the optimal.
A high-performance, flexible, and potentially inexpensive speech recognition system is described. The system is based on two special-purpose integrated circuits that perform the speech recognition algorithms very effi...
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A high-performance, flexible, and potentially inexpensive speech recognition system is described. The system is based on two special-purpose integrated circuits that perform the speech recognition algorithms very efficiently. One of these integrated circuits is the front-end processor, which computes spectral coefficients from incoming speech. The second integrated circuit computes a dynamic-time-warp algorithm. The system can compare an input word with 1000-word templates and respond to a user within \fracalgorithm{4} s. The system demonstrates that computational complexity need not be a major limiting factor in the design of speech recognition systems.
Editor?s note:Today?s complex SoCs need sophisticated infrastructure IP, not only to test and diagnose embedded memories but also to repair them and improve fabrication yield. The authors? solution integrates memory I...
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Editor?s note:Today?s complex SoCs need sophisticated infrastructure IP, not only to test and diagnose embedded memories but also to repair them and improve fabrication yield. The authors? solution integrates memory IP with test and repair IP in a composite infrastructure IP that ensures manufacturing and field repair efficiency and optimizes SoC yield.--Paolo Prinetto, Politecnico di Torino
The problem of designing nonuniformly spaced arrays is formulated as one of constrained optimization in which the cost function is chosen to select the array with the minimum number of elements. The response of the ar...
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The problem of designing nonuniformly spaced arrays is formulated as one of constrained optimization in which the cost function is chosen to select the array with the minimum number of elements. The response of the array is controlled by a set of inequality point response constraints. It is shown that a suitable cost function for this problem is the l(p) quasi-norm for 0 < p < 1 and that there exists a number p1 such that for all 0 < p < p1 the resulting array is maximally sparse. Furthermore it is shown that a solution to the problem lies at an extreme point of the simplex formed by the point constaints. A simplex search algorithm is described which will converge to a local minimum of the cost function on this simplex. The algorithm is illustrated in application to the design of sparse linear and planar arrays.
A fuzzy statistical normalization fuzzy constant false alarm rate (FSNF-CFAR) detector in a K distribution background based on fuzzy statistical normalization and fuzzy soft decision is proposed. The performance of th...
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A fuzzy statistical normalization fuzzy constant false alarm rate (FSNF-CFAR) detector in a K distribution background based on fuzzy statistical normalization and fuzzy soft decision is proposed. The performance of the proposed fuzzy soft decision detector is studied both for homogeneous backgrounds and for nonhomogeneous environments caused by interfering targets or clutter edges. Performance comparisons with conventional hard decision CFAR detectors such as cell averaging CFAR (CA-CFAR), greater of CFAR (GO-CFAR), and ordered statistics CFAR (OS-CFAR) are carried out. The simulation shows that the proposed FSNF-CFAR detector is simple and efficient, and the comparison results show that it not only can get good detection performance in homogeneous K distribution backgrounds but also can confront interfering targets and clutter edges at the same time in nonhomogeneous environments. Moreover, the fuzzy soft decision detector can provide more valuable information than the hard decision detector for data fusion, target tracking, or object identification.
In active sensing, transmitters emit probing waveforms into the environment. The probing waveforms interact with scatters that reflect distorted copies of the waveforms. Receivers then measure the distorted copies to ...
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In active sensing, transmitters emit probing waveforms into the environment. The probing waveforms interact with scatters that reflect distorted copies of the waveforms. Receivers then measure the distorted copies to infer information about the environment. The choice of the probing waveform is important because it affects slant range resolution, Doppler tolerance, clutter, and electronic countermeasures. A traditional performance metric for the probing waveform is the ambiguity function, which describes the correlation between the waveform and a delayed and (narrowband) Doppler shifted copy of the same waveform [1]. The direct synthesis of a waveform given a desired ambiguity function is exceedingly difficult [2]. Often designers focus on optimizing only the waveform?s autocorrelation function (which is the zero Doppler cut of the ambiguity function). Any method that optimizes the autocorrelation function is implicitly performing spectral shaping by trying to flatten the passband of the waveform?s spectrum [1], [2].
Energy conservation is an important concern in wireless networks. Many algorithms for constructing a broadcast tree with minimum energy consumption and other goals have been developed. However, no previous research wo...
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Energy conservation is an important concern in wireless networks. Many algorithms for constructing a broadcast tree with minimum energy consumption and other goals have been developed. However, no previous research work considers the total energy consumption and transmission delays of the broadcast tree simultaneously. In this paper, based on an (alpha, beta)-tree, a novel concept to wireless networks, we define a new Strongly connected Broadcast Arborescence with bounded Transmission delay (SBAT) problem and design the Strongly connected Broadcast Arborescence (SBA) algorithm with linear running time to construct a strongly connected broadcast tree with bounded total power, while satisfying the constraint that the transmission delays between the source and the other hosts are also bounded. We also propose the distributed version of the SBA algorithm. The theoretical analysis and simulation results show that the SBA algorithm gives a proper solution to the SBAT problem.
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